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“Lying” in computer-generated texts: hallucinations and omissions
There is huge excitement about ChatGPT and other large generative language models that produce fluent and human-like texts in English and other human languages. But these models have one big drawback, which is that their texts can be factually incorrect (hallucination) and also leave out key information (omission).
In our chapter for The Oxford Handbook of Lying, we look at hallucinations, omissions, and other aspects of “lying” in computer-generated texts. We conclude that these problems are probably inevitable.
Omissions are inevitable because a computer system cannot cram all possibly-relevant information into a text that is short enough to be actually read. In the context of summarising medical information for doctors, for example, the computer system has access to a huge amount of patient data, but it does not know (and arguably cannot know) what will be most relevant to doctors.
Hallucinations are inevitable because of flaws in computer systems, regardless of the type of system. Systems which are explicitly programmed will suffer from software bugs (like all software systems). Systems which are trained on data, such as ChatGPT and other systems in the Deep Learning tradition, “hallucinate” even more. This happens for a variety of reasons. Perhaps most obviously, these systems suffer from flawed data (e.g., any system which learns from the Internet will be exposed to a lot of false information about vaccines, conspiracy theories, etc.). And even if a data-oriented system could be trained solely on bona fide texts that contain no falsehoods, its reliance on probabilistic methods will mean that word combinations that are very common on the Internet may also be produced in situations where they result in false information.
Suppose, for example, on the Internet, the word “coughing” is often followed by “… and sneezing.” Then a patient may be described falsely, by a data-oriented system, as “coughing and sneezing” in situations where they cough without sneezing. Problems of this kind are an important focus for researchers working on generative language models. Where this research will lead us is still uncertain; the best one can say is that we can try to reduce the impact of these issues, but we have no idea how to completely eliminate them.
“Large generative language models’ texts can be factually incorrect (hallucination) and leave out key information (omission).”
The above focuses on unintentional-but-unavoidable problems. There are also cases where a computer system arguably should hallucinate or omit information. An obvious example is generating marketing material, where omitting negative information about a product is expected. A more subtle example, which we have seen in our own work, is when information is potentially harmful and it is in users’ best interests to hide or distort it. For example, if a computer system is summarising information about sick babies for friends and family members, it probably should not tell an elderly grandmother with a heart condition that the baby may die, since this could trigger a heart attack.
Now that the factual accuracy of computer-generated text draws so much attention from society as a whole, the research community is starting to realize more clearly than before that we only have a limited understanding of what it means to speak the truth. In particular, we do not know how to measure the extent of (un)truthfulness in a given text.
To see what we mean, suppose two different language models answer a user’s question in two different ways, by generating two different answer texts. To compare these systems’ performance, we would need a “score card” that allowed us to objectively score the two texts as regards their factual correctness, using a variety of rubrics. Such a score card would allow us to record how often each type of error occurs in a given text, and aggregate the result into an overall truthfulness score for that text. Of particular importance would be the weighing of errors: large errors (e.g., a temperature reading that is very far from the actual temperature) should weigh more heavily than small ones, key facts should weigh more heavily than side issues, and errors that are genuinely misleading should weigh more heavily than typos that readers can correct by themselves. Essentially, the score card would work like a fair school teacher who marks pupils’ papers.
We have developed protocols for human evaluators to find factual errors in generated texts, as have other researchers, but we cannot yet create a score card as described above because we cannot assess the impact of individual errors.
What is needed, we believe, is a new strand of linguistically informed research, to tease out all the different parameters of “lying” in a manner that can inform the above-mentioned score cards, and that may one day be implemented into a reliable fact-checking protocol or algorithm. Until that time, those of us who are trying to assess the truthfulness of ChatGPT will be groping in the dark.
Featured image by Google DeepMind Via Unsplash (public domain)
Is a 15-week limit on abortion an acceptable compromise?
A recent opinion piece by George F. Will, “Ambivalent about abortion, the American middle begins to find its voice” in the Washington Post made the startling claim that the overturning of Roe v. Wade (Dobbs v. Jackson Women’s Health Organization, 2022) has resulted in “a partial healing of the nation’s civic culture.” One might think exactly the reverse. The Dobbs decision energized voters, especially women and young people, resulting in numerous Republican electoral defeats across the country. However, Will argues that the return of abortion policy to the states gives voters the opportunity of choosing moderate restrictions on abortion. Since most Americans support early abortion while opposing late-gestation abortion, Will thinks that a 15-week ban on abortion would be an acceptable compromise.
Why 15 weeks? Two reasons can be given. Almost all abortions in the US—93%—occur within the first 15 weeks of pregnancy. For this reason, making abortion illegal after 15 weeks would not, it would seem, impose serious burdens on most people seeking abortions.
Another reason is that several European countries limit abortion on request to the first trimester, leading some US lawmakers to suggest that a 15-week ban would bring our abortion law in line with theirs. This is disingenuous, to say the least. While elective abortion is limited in some European countries, it is not banned afterwards, but is allowed on other grounds, including economic or social reasons, or a threat to the woman’s physical or mental health. Moreover, in most European countries, patients do not have to pay for abortion; it is covered under universal health coverage. The fact is that the trend in Europe has not been to limit abortion, but to expand access to it. Countries in Europe “… have removed bans, increased abortion’s legality and taken steps to ensure laws and policies on abortion are guided by public health evidence and clinical best practices.”
Were states to guarantee access to abortion prior to 15 weeks, a 15-week ban might be acceptable. However, even before Dobbs, many women in the US lacked access to abortion, due to a dearth of providers, especially in rural areas. They often had to travel many miles to find an abortion clinic, which meant that they had to arrange childcare if they have other children or take time off work. Delay is also caused by the need to raise money for an abortion, which is not paid for by Medicaid in most states, except in cases of rape, incest, or a life-threatening condition. To be sure, even if there were none of these roadblocks, some women would still not be able to have early abortions because they do not know that they are pregnant, due to youth, being menopausal, chronic obesity, or a lack of pregnancy symptoms. Any time limits will pose hardships for some people. But if access to early abortions were guaranteed, a compromise on a 15-week limit might be worth it.
I suspect that time-limit advocates are not particularly interested in making sure that women who have abortions get them early in pregnancy. They want to place roadblocks in the way of getting abortions, full stop. That these roadblocks increase the numbers of late abortions is of little concern to them, however much they wring their hands over late abortions. Abortion can be reduced by reducing the number of unwanted pregnancies, something that has been shown to be achieved by access to contraceptives and science-based sex education in the schools. Remember when pro-lifers emphasized those methods? Me neither.
“Some US lawmakers suggest that a 15-week ban would bring our abortion law in line with European countries. This is disingenuous, to say the least.”
My second concern is with abortions sought after 15 weeks. The reason for a late abortion may be that the woman has a medical condition that has not developed, or has not been detected, until later in pregnancy. In such cases, the pregnancy is almost always a wanted pregnancy, and the decision to terminate imposes a tragic choice.
It may be responded that all states allow abortions to be performed when this is necessary to save the pregnant woman’s life, and many allow for abortions to protect her from a serious health risk. The problem is that these exceptions conflict with standard medical care, especially in the case of miscarriage. Once the woman has begun to miscarry, the failure to remove the fetus is likely to cause her sepsis, which can be life-threatening. However, in states with restrictive abortion laws, doctors cannot perform an immediate abortion, which is the standard of care in such situations. They have to wait until her death is imminent and, in some states, they cannot remove the fetus until its heart stops.
Ireland’s restrictive abortion law was repealed after a woman who was denied an abortion during a miscarriage died from septicemia. To the best of my knowledge, no woman in the US has died as a result of restrictive abortion laws, but some have come close. An OB-GYN in San Antonio had to wait until the fetal heartbeat stopped to treat a miscarrying patient who developed a dangerous womb infection. The delay caused complications which required her to have surgery, lose multiple liters of blood, and be put on a breathing machine. Texas law essentially requires doctors to commit malpractice.
Conservatives often portray those in the pro-choice camp as advocating abortion until the day of delivery, for trivial reasons. This is deeply unfair. If they want us to compromise on time limits, they should be willing to guarantee access to abortion before 15 weeks. They should be willing to compromise on pregnancy prevention through contraception and sex education. And they should agree to drop all restrictions on late-term abortions that make legislators, rather than doctors, in charge of deciding what is appropriate medical care for their patients.
Featured image: Gayatri Malhotra via Unsplash (public domain)
Real patterns and the structure of language
There’s been a lot of hype recently about the emergence of technologies like ChatGPT and the effects they will have on science and society. Linguists have been especially curious about what highly successful large language models (LLMs) mean for their business. Are these models unearthing the hidden structure of language itself or just marking associations for predictive purposes?
In order to answer these sorts of questions we need to delve into the philosophy of what language is. For instance, if Language (with a big “L”) is an emergent human phenomenon arising from our communicative endeavours, i.e. a social entity, then AI is still some ways off approaching it in a meaningful way. If Chomsky, and those who follow his work, are correct that language is a modular mental system innately given to human infants and activated by miniscule amounts of external stimulus, then AI is again unlikely to be linguistic, since most of our most impressive LLMs are sucking up so many resources (both in terms of data and energy) that they are far from this childish learning target. On the third hand, if languages are just very large (possibly infinite) collections of sentences produced by applying discrete rules, then AI could be super-linguistic.
In my new book, I attempt to find a middle ground or intersection between these views. I start with an ontological picture (meaning a picture of what there is “out there”) advocated in the early nineties by the prominent philosopher and cognitive scientist, Daniel Dennett. He draws from information theory to distinguish between noise and patterns. In the noise, nothing is predictable, he says. But more often than not, we can and do find regularities in large data structures. These regularities provide us with the first steps towards pattern recognition. Another way to put this is that if you want to send a message and you need the entire series (string or bitmap) of information to do so, then it’s random. But if there’s some way to compress the information, it’s a pattern! What makes a pattern real, is whether or not it needs an observer for its existence. Dennett uses this view to make a case for “mild realism” about the mind and the position (which he calls the “intentional stance”) we use to identify minds in other humans, non-humans, and even artifacts. Basically, it’s like a theory we use to predict behaviour based on the success of our “minded” vocabulary comprising beliefs, desires, thoughts, etc. For Dennett, prediction matters theoretically!
If it’s not super clear yet, consider a barcode. At first blush, the black lines of varying length set to a background of white might seem random. But the lines (and spaces) can be set at regular intervals to reveal an underlying pattern that can be used to encode information (about the labelled entity/product). Barcodes are unique patterns, i.e. representations of the data from which more information can be drawn (by the way Nature produces these kinds of patterns too in fractal formation).
“The methodological chasm between theoretical and computational linguistics can be surmounted.”
I adapt this idea in two ways in light of recent advances in computational linguistics and AI. The first reinterprets grammars, specifically discrete grammars of theoretical linguistics, as compression algorithms. So, in essence, a language is like a real pattern. Our grammars are collections of rules that compress these patterns. In English, noticing that a sentence is made up of a noun phrase and verb phrase is such a compression. More complex rules capture more complex patterns. Secondly, discrete rules are just a subset of continuous processes. In other words, at one level information theory looks very statistical while generative grammar looks very categorical. But the latter is a special case of the former. I show in the book how some of the foundational theorems of information theory can be translated to discrete grammar representations. So there’s no need to banish the kinds of (stochastic) processes often used and manipulated in computational linguistics, as many theoretical linguists have been wont to do in the past.
This just means that the methodological chasm between theoretical and computational linguistics, which has often served to close the lines of communication between the fields, can be surmounted. Ontologically speaking, languages are not collections of sentences, minimal mental structures, or social entities by themselves. They are informational states taken from complex interactions of all of the above and more (like the environment). On this view, linguistics quickly emerges as a complexity science in which the tools of linguistic grammars, LLMs, and sociolinguistic observations all find a homogeneous home. Recent work on complex systems, especially in biological systems theory, has breathed new life into this interdisciplinary field of inquiry. I argue that the study of language, including the inner workings of both the human mind and ChatGPT, belong within this growing framework.
For decades, computational and theoretical linguists have been talking different languages. The shocking syntactic successes of modern LLMs and ChatGPT have forced them into the same room. Realising that languages are real patterns emerging from biological systems gets someone to break the awkward silence…
Featured image by Google DeepMind Via Unsplash (public domain)
Real patterns and the structure of language
There’s been a lot of hype recently about the emergence of technologies like ChatGPT and the effects they will have on science and society. Linguists have been especially curious about what highly successful large language models (LLMs) mean for their business. Are these models unearthing the hidden structure of language itself or just marking associations for predictive purposes?
In order to answer these sorts of questions we need to delve into the philosophy of what language is. For instance, if Language (with a big “L”) is an emergent human phenomenon arising from our communicative endeavours, i.e. a social entity, then AI is still some ways off approaching it in a meaningful way. If Chomsky, and those who follow his work, are correct that language is a modular mental system innately given to human infants and activated by miniscule amounts of external stimulus, then AI is again unlikely to be linguistic, since most of our most impressive LLMs are sucking up so many resources (both in terms of data and energy) that they are far from this childish learning target. On the third hand, if languages are just very large (possibly infinite) collections of sentences produced by applying discrete rules, then AI could be super-linguistic.
In my new book, I attempt to find a middle ground or intersection between these views. I start with an ontological picture (meaning a picture of what there is “out there”) advocated in the early nineties by the prominent philosopher and cognitive scientist, Daniel Dennett. He draws from information theory to distinguish between noise and patterns. In the noise, nothing is predictable, he says. But more often than not, we can and do find regularities in large data structures. These regularities provide us with the first steps towards pattern recognition. Another way to put this is that if you want to send a message and you need the entire series (string or bitmap) of information to do so, then it’s random. But if there’s some way to compress the information, it’s a pattern! What makes a pattern real, is whether or not it needs an observer for its existence. Dennett uses this view to make a case for “mild realism” about the mind and the position (which he calls the “intentional stance”) we use to identify minds in other humans, non-humans, and even artifacts. Basically, it’s like a theory we use to predict behaviour based on the success of our “minded” vocabulary comprising beliefs, desires, thoughts, etc. For Dennett, prediction matters theoretically!
If it’s not super clear yet, consider a barcode. At first blush, the black lines of varying length set to a background of white might seem random. But the lines (and spaces) can be set at regular intervals to reveal an underlying pattern that can be used to encode information (about the labelled entity/product). Barcodes are unique patterns, i.e. representations of the data from which more information can be drawn (by the way Nature produces these kinds of patterns too in fractal formation).
“The methodological chasm between theoretical and computational linguistics can be surmounted.”
I adapt this idea in two ways in light of recent advances in computational linguistics and AI. The first reinterprets grammars, specifically discrete grammars of theoretical linguistics, as compression algorithms. So, in essence, a language is like a real pattern. Our grammars are collections of rules that compress these patterns. In English, noticing that a sentence is made up of a noun phrase and verb phrase is such a compression. More complex rules capture more complex patterns. Secondly, discrete rules are just a subset of continuous processes. In other words, at one level information theory looks very statistical while generative grammar looks very categorical. But the latter is a special case of the former. I show in the book how some of the foundational theorems of information theory can be translated to discrete grammar representations. So there’s no need to banish the kinds of (stochastic) processes often used and manipulated in computational linguistics, as many theoretical linguists have been wont to do in the past.
This just means that the methodological chasm between theoretical and computational linguistics, which has often served to close the lines of communication between the fields, can be surmounted. Ontologically speaking, languages are not collections of sentences, minimal mental structures, or social entities by themselves. They are informational states taken from complex interactions of all of the above and more (like the environment). On this view, linguistics quickly emerges as a complexity science in which the tools of linguistic grammars, LLMs, and sociolinguistic observations all find a homogeneous home. Recent work on complex systems, especially in biological systems theory, has breathed new life into this interdisciplinary field of inquiry. I argue that the study of language, including the inner workings of both the human mind and ChatGPT, belong within this growing framework.
For decades, computational and theoretical linguists have been talking different languages. The shocking syntactic successes of modern LLMs and ChatGPT have forced them into the same room. Realising that languages are real patterns emerging from biological systems gets someone to break the awkward silence…
Featured image by Google DeepMind Via Unsplash (public domain)
What can Large Language Models offer to linguists?
It is fair to say that the field of linguistics is hardly ever in the news. That is not the case for language itself and all things to do with language—from word of the year announcements to countless discussions about grammar peeves, correct spelling, or writing style. This has changed somewhat recently with the proliferation of Large Language Models (LLMs), and in particular since the release of OpenAI’s ChatGPT, the best-known language model. But does the recent, impressive performance of LLMs have any repercussions for the way in which linguists carry out their work? And what is a Language Model anyway?
At heart, all an LLM does is predict the next word given a string of words as a context —that is, it predicts the next, most likely word. This is of course not what a user experiences when dealing with language models such as ChatGPT. This is on account of the fact that ChatGPT is more properly described as a “dialogue management system”, an AI “assistant” or chatbot that translates a user’s questions (or “prompts”) into inputs that the underlying LLM can understand (the latest version of OpenAI’s LLM is a fine-tuned version of GPT-4).
“At heart, all an LLM does is predict the next word given a string of words as a context.”
An LLM, after all, is nothing more than a mathematical model in terms of a neural network with input layers, output layers, and many deep layers in between, plus a set of trained “parameters.” As the computer scientist Murray Shanahan has put it in a recent paper, when one asks a chatbot such as ChatGPT who was the first person to walk on the moon, what the LLM is fed is something along the lines of:
Given the statistical distribution of words in the vast public corpus of (English) text, what word is most likely to follow the sequence “The first person to walk on the Moon was”?
That is, given an input such as the first person to walk on the Moon was, the LLM returns the most likely word to follow this string. How have LLMs learned to do this? As mentioned, LLMs calculate the probability of the next word given a string of words, and it does so by representing these words as vectors of values from which to calculate the probability of each word, and where sentences can also be represented as vectors of values. Since 2017, most LLMs have been using “transformers,” which allow the models to carry out matrix calculations over these vectors, and the more transformers are employed, the more accurate the predictions are—GPT-3 has some 96 layers of such transformers.
The illusion that one is having a conversation with a rational agent, for it is an illusion, after all, is the result of embedding an LLM in a larger computer system that includes background “prefixes” to coax the system into producing behaviour that feels like a conversation (the prefixes include templates of what a conversation looks like). But what the LLM itself does is generate sequences of words that are statistically likely to follow from a specific prompt.
It is through the use of prompt prefixes that LLMs can be coaxed into “performing” various tasks beyond dialoguing, such as reasoning or, according to some linguists and cognitive scientists, learn the hierarchical structures of a language (this literature is ever increasing). But the model itself remains a sequence predictor, as it does not manipulate the typical structured representations of a language directly, and it has no understanding of what a word or a sentence means—and meaning is a crucial property of language.
An LLM seems to produce sentences and text like a human does—it seems to have mastered the rules of the grammar of English—but at the same time it produces sentences based on probabilities rather on the meanings and thoughts to express, which is how a human person produces language. So, what is language so that an LLM could learn it?
“An LLM seems to produce sentences like a human does but it produces them based on probabilities rather than on meaning.”
A typical characterisation of language is as a system of communication (or, for some linguists, as a system for having thoughts), and such a system would include a vocabulary (the words of a language) and a grammar. By a “grammar,” most linguists have in mind various components, at the very least syntax, semantics, and phonetics/phonology. In fact, a classic way to describe a language in linguistics is as a system that connects sound (or in terms of other ways to produce language, such as hand gestures or signs) and meaning, the connection between sound and meaning mediated by syntax. As such, every sentence of a language is the result of all these components—phonology, semantics, and syntax—aligning with each other appropriately, and I do not know of any linguistic theory for which this is not true, regardless of differences in focus or else.
What this means for the question of what LLMs can offer linguistics, and linguists, revolves around the issue of what exactly LLMs have learned to begin with. They haven’t, as a matter of fact, learned a natural language at all, for they know nothing about phonology or meaning; what they have learned is the statistical distribution of the words of the large texts they have been fed during training, and this is a rather different matter.
As has been the case in the past with other approaches in computational linguistics and natural language processing, LLMs will certainly flourish within these subdisciplines of linguistics, but the daily work of a regular linguist is not going to change much any time soon. Some linguists do study the properties of texts, but this is not the most common undertaking in linguistics. Having said that, how about the opposite question: does a run-of-the-mill linguist have much to offer to LLMs and chatbots at all?
Featured image: Google Deepmind via Unsplash (public domain)
Elon Musk, Mars, and bioethics: is sending astronauts into space ethical?
The recent crash of the largest-ever space rocket, Starship, developed by Elon Musk’s SpaceX company, has certainly somewhat disrupted optimism about the human mission to Mars that is being prepared for the next few years. It is worth raising the issue of the safety of future participants in long-term space missions, especially missions to Mars, on the background of this disaster. And it is not just about safety from disasters like the one that happened to Musk. Protection from the negative effects of prolonged flight in zero gravity, protection from cosmic radiation, as well as guaranteeing sufficiently high crew productivity over the course of a multi-year mission also play an important role.
Fortunately, no one was killed in the aforementioned crash, as it was a test rocket alone without a crew. However, past disasters in which astronauts died, such as the Space Shuttle Challenger and Space Shuttle Columbia disasters, remind us that it is the seemingly very small details that determine life and death. So far, 15 astronauts and 4 cosmonauts have died in space flights. 11 more have died during testing and training on Earth. It is worth mentioning that space flights are peacekeeping missions, not military operations. They are carried out relatively infrequently and by a relatively small number of people.
It is also worth noting the upcoming longer and more complex human missions in the near future, such as the mission to Mars. The flight itself, which is expected to last several months, is quite a challenge, and disaster can happen both during takeoff on Earth, landing on Mars, and then on the way back to Earth. And then there are further risks that await astronauts in space.
The first is exposure to galactic cosmic radiation and solar energetic particles events, especially during interplanetary flight, when the crew is no longer protected by both Earth’s magnetic field and a possible shelter on Mars. Protection from cosmic radiation for travel to Mars is a major challenge, and 100% effective protective measures are still lacking. Another challenge remains being in long-term zero-gravity conditions during the flight, followed by altered gravity on Mars. Bone loss and muscle atrophy are the main, but not only, negative effects of being in these states. Finally, it is impossible to ignore the importance of psychological factors related to stress, isolation, being in an enclosed small space, distance from Earth.
A human mission to Mars, which could take about three years, brings with it a new type of danger not known from the previous history of human space exploration. In addition to the aforementioned amplified impact of factors already known—namely microgravity, cosmic radiation, and isolation—entirely new risk factors are emerging. One of them is the impossibility of evacuating astronauts in need back to Earth, which is possible in missions carried out at the International Space Station. It seems that even the best-equipped and trained crew may not be able to guarantee adequate assistance to an injured or ill astronaut, which could lead to her death—assuming that care on Earth would guarantee her survival and recovery. Another problem is the delay in communication, which will reach tens of minutes between Earth and Mars. This situation will affect the degree of autonomy of the crew, but also their responsibility. Wrong decisions, made under conditions of uncertainty, can have not only negative consequences for health and life, but also for the entire mission.
“It is worth raising the question of the ethicality of the decision to send humans into such a dangerous environment.”
Thus, we can see that a future human mission to Mars will be very dangerous, both as a result of factors already known but intensified, as well as new risk factors. It is worth raising the question of the ethicality of the decision to send humans into such a dangerous environment. The ethical assessment will depend both on the effectiveness of available countermeasures against harmful factors in space and also on the desirability and justification for the space missions themselves.
Military ethics and bioethics may provide some analogy here. In civilian ethics and bioethics, we do not accept a way of thinking and acting that would mandate the subordination of the welfare, rights, and health of the individual to the interests of the group. In military ethics, however, this way of thinking is accepted, formally in the name of the higher good. Thus, if the mission to Mars is a civilian mission, carried out on the basis of values inherent in civilian ethics and bioethics rather than military ethics, it may be difficult to justify exposing astronauts to serious risks of death, accident, and disease.
One alternative may be to significantly postpone the mission until breakthrough advances in space technology and medicine can eliminate or significantly reduce the aforementioned risk factors. Another alternative may be to try to improve astronauts through biomedical human enhancements. Just as in the army there are known methods of improving the performance of soldiers through pharmacological means, analogous methods could be applied to future participants in a mission to Mars. Perhaps more radical, and thus controversial, methods such as gene editing would be effective, assuming that gene editing of selected genes can enhance resistance to selected risk factors in space.
But the idea of genetically modifying astronauts, otherwise quite commonsensical, given also the cost of such a mission, as well as the fact that future astronauts sent to Mars would likely be considered representative of the great effort of all humanity, raises questions about the justification for such a mission. What do the organizers of a mission to Mars expect to achieve? Among the goals traditionally mentioned are the scientific merits of such a mission, followed by possible commercial applications for the future. Philosophers, as well as researchers of global and existential catastrophes, often discuss the concept of space refuge, in which the salvation of the human species in the event of a global catastrophe on Earth would be possible only by settling somewhere beyond Earth. However, it seems that the real goals in our non-ideal society will be political and military.
Beaumarchais and <em>Electronic Enlightenment</em>
The addition to Electronic Enlightenment (EE) of nearly 500 letters from the Beaumarchais correspondence is a significant event in eighteenth-century studies. Drawn from the second volume of Gunnar and Mavis von Proschwitz’s edited collection, Beaumarchais and the “Courrier de lÉurope”, first published thirty years ago in Studies on Voltaire and the Eighteenth Century, these letters join with 175 letters from the first volume (previously included in EE). The total of 660 letters in this collection include a combination of letters printed in that periodical and letters from public and private collections. (In 2005, von Proschwitz published a selection of 107 of these in a French edition, entitled Lettres de Combat.)
Collectively the letters being published by EE represent the largest tranche of Beaumarchais letters available for online research; moreover, they constitute approximately one third of Beaumarchais letters published to date and over one sixth of all known Beaumarchais letters in existence.
In the context of eighteenth-century correspondences, the Beaumarchais archive stands out for several reasons. The first is the volume of the archive. The known portion of the Beaumarchais papers is over 4,500 documents, constituting one of the largest corpora of eighteenth-century papers known. The full archive, if ever fully inventoried and edited, would run somewhere between 6,000 and 20,000 documents. At the upper range it would become among the largest known archives of personal papers of the period.
The second is geographical breadth—from Vienna to Madrid to the Netherlands to England and North America, the Beaumarchais correspondence is important because it shows how actually we limit our understanding if we focus on solely “French” or “Francophone” correspondence networks.
The third is sociological breadth—Beaumarchais as an historical figure offers us insights into the eighteenth century that stand apart from the major figures whose correspondence has been edited and studied. He was an artisan, a musician, a financier, commercial entrepreneur, printer, investor, politician, judge, diplomat, spy, litigant, criminal (he was imprisoned in at least four capitals), husband, lover, brother, father and, of course, a playwright. His correspondence, and thus the network of correspondents connected him to a wider swath of eighteenth-century European and North American society than almost all personal correspondences studied to date, rivaling and perhaps exceeding the Franklin and Jefferson papers in this respect.
The editorial history of the Beaumarchais correspondence extends over two centuries of literary and political history. Since 1809, when the first edition of Beaumarchais’s Oeuvres was published, over 1,500 letters have been edited—though most of them not with the critical apparatus of the Proschwitz letters published by EE—over the course of more than two centuries.
Nearly 500 letters were printed in partial editions of Beamarchais’ work or correspondence, from 1809 to 1929. The first edition of his complete works edited by his amanuensis, Gudin de la Brenellerie (seven volumes, 1809), included 55 letters that Gudin had transcribed. A second edition, by the journalist, historian and politician Saint-Marc de Girardin in 1837 included 53 additional letters. A collection of 29 letters from the Comedie Francaise archives were published in the Revue Retrospective (1836). In his two-volume biography, Beaumarchais et son temps (1858), Louis de Loménie, referenced and included partial transcripts of hundreds of letters, but included in the appendix only 35 complete texts of previously unedited letters. A second biographer, Eugène Lentilhac, in his Beaumarchais et ses oeuvres (1887), included 12 partially transcribed letters not previously published. In 1890, Louis Bonneville de Marsagny published a biography of Beaumarchais’s third (and longest lasting) wife, Marie Thérèse Willermalauz, and claimed to have consulted “sa correspondance inédite” though no letters are reproduced or directly referenced.
In the early twentieth century, the first effort to produce a complete edition of the correspondence was made by Louis Thomas; however, as he explains in the preface to his edition entitled Lettres de Jeunesse (1923), his military service during the Great War put an end to his research; so in 1923 he published 167 letters from the first two decades of Beaumarchais’ adult life, some of which had been previously published. Several years later, in 1929, the eminent French literature scholar in the United States of the day, Gilbert Chinard, edited a collection of Lettres inédites de Beaumarchais consisting of 109 letters acquired by the Clements Library at the University of Michigan; these consisted of letters to his wife and daughter.
In more recent decades, over 1,000 additional items have been published, between the edition launched by Brian Morton in 1968, continued by Donald Spinelli, which added an additional 300 previously unpublished letters over four volumes of Correspondence, and then in 1990, the Proschwitz edition.
Proschwitz, a noted philologist, added to these letters the most extensive critical apparatus associated with any edition of Beaumarchais letters. He did not seek to produce a critical edition or a material bibliography of these letters, approaches that are difficult to apply to eighteenth-century correspondence in general and to the Beaumarchais archive in particular. Rather, Proschwitz in his notes emphasized the significance of these documents for our understanding of Beaumarcahis’ life and of the eighteenth century. In these letters, we see Beaumarchais not only as a playwright seeking to circumvent censorship to have Marriage de Figaro finally staged, but also as an entrepreneur, a printer, an urban property owner, an emissary, and a transatlantic merchant. Through this window we have a window on the eighteenth century that is geographically, socially, and culturally much broader and more diverse than what we generally encounter through the correspondences previously published in EE.
With the appearance of these letters and the launching of the first new projects on Beaumarchais’s correspondence in 50 years, including the effort spearheaded by Linda Gil to produce a definitive inventory with a material bibliography, and my own work to analyze the network of correspondents from the known correspondence, this publication in EE offers eighteenth-century scholars new reason to consider a longstanding, but still little understood, figure of the age.
A version of this blog post was first published on Electronic Enlightenment.
Featured image by Debby Hudson on Unsplash (public domain)
What is subject marketing? An interview with Hana Purslow, philosophy marketing manager
In this interview, our marketing manager for philosophy, Hana Purslow, outlines OUP’s approach to subject marketing. She provides examples of campaigns and channels that we use to promote our content and reveals what she enjoys most about her job and how she sees her role evolving in the coming years.
Subject marketing promotes a particular topic or subject to a research community by showcasing relevant content in that area, regardless of format. This can include book chapters, journal papers, or articles from our reference products. By marketing the subject, we showcase key content for academics to use in their teaching or research. Through the dissemination of OUP’s scholarly research in this targeted way, we can have a direct and positive impact within the subject community.
Our focus in subject marketing is building our brand and OUP’s profile in a particular discipline. We concentrate our efforts on building a strong subject community around our content; it is only through researchers reading, sharing, and citing our content that we can be sure of the real-world impact our publishing has. Our job in marketing is to ensure that the academic community is aware of OUP’s high-quality, cutting-edge, and impactful research.
I am responsible for leading our philosophy subject marketing. I take huge enjoyment in working with OUP philosophy content and learning from the greatest minds across all areas of philosophy. I find working with OUP Philosophy Editors Peter Momtchiloff, Peter Ohlin, and Lucy Randall extremely rewarding—their knowledge of philosophy is second to none, and with so many interesting areas within the subject to cover, it’s a marketer’s dream when choosing which topics to highlight!
A good example is the “Philosophy in Focus” campaign I run each month. This is our most successful thematic campaign, where we host a selection of thought-provoking free content around a particular theme. We market each collection through a range of channels including social media, advertising, email, and a dedicated collection web page—always evaluating our results to hone our strategies for increased reach, awareness, and engagement with our OUP philosophy content.
Our first topic was “race,” which we launched during Black History Month in October 2021. Other topics, which often coincide with observance months, include emotions, disability, feminism, technology and AI, and democracy.
“Through subject marketing, academics can discover our new research in an engaging way, with a topic lens.”
Choosing content around important events or areas in philosophy is a team effort between myself in marketing and my colleagues in editorial, ensuring we share key research within the subject and shine a light on topics that matter. This means that academics can discover our new research in an engaging way, with a topic lens beyond individual titles. Sharing content in a way that engages our audience and showcases the OUP Philosophy brand in the best possible light is something I take great pride in. I am grateful to have so many brilliant authors to work with and feel privileged to share their influential work in campaigns like Philosophy in Focus.
The OUPblog—this really helps to increase the discoverability of our content, which is how our books are found on search engines like Google. As we publish across such a broad range of topics, we can get creative when working with authors on blog posts. For instance, in our most read philosophy blog of 2022, Kristin Gjesdal brings a unique viewpoint to the relationship between philosophy and theatre by focusing on the work of playwright Henrik Isben.
Our philosophy blog posts aim to be accessible to non-specialist academics so have a wider reach than our published content itself. We share OUPblog posts widely on social media, particularly through our dedicated philosophy Twitter channel, @OUPPhilosophy.
“We choose channels for promoting our content based on what we want the broader campaign to achieve.”
It’s worth noting that we choose channels for our content based on what we want the broader campaign to achieve, so while not all our campaigns will feature a blog post, this doesn’t make them any less valuable—we align the strategy or channel with the campaign objective.
I have always been passionate about marketing, which led to numerous paths in the events, public, and private sectors before joining the publishing industry, which I soon realized was the place for me! Working in marketing for an academic publisher means that I can give back to the world by helping to advance knowledge and learning.
At OUP, we use digital marketing to share high-quality academic content from thought-leading authors around the world. This is something that really drives me; being a lover of all things digital, targeted marketing allows me to build creative strategies that showcase the breadth of publishing we have to offer at OUP, alongside our vision to grow and maximize the impact of our content.
A large part of my role involves working with authors, which is a real highlight for me. I help authors learn how to promote their books by providing guidance and explaining how they can build their profile to increase long-term engagement with their work. Seeing this come to fruition is so rewarding!
Creating campaigns for conferences—such as American Philosophical Association, Philosophy of Science Association, or The Joint Session of the Aristotelian Society and the Mind Association—is another highlight. Whether we’re attending in person or promoting our content digitally, it’s exciting to showcase our cutting-edge publishing with conference delegates and researchers from around the world.
“I help authors learn how to promote their books and build their profile. Seeing this come to fruition is so rewarding!”
Overall, I’m motivated by seeing the impact that marketing can make, from campaign planning right through to the end results. It’s exciting to see how marketing can influence online usage and more, depending on what we’re trying to achieve for that campaign.
Everything evolves, and so does my role and offering as a marketer. This will be based on the challenges and opportunities we face in the market—from open access growth to the way people find research, and the ever-changing digital landscape. There is always more to learn, and I am excited to see what’s coming next.
“Of Socrates we have nothing genuine but in the Memorabilia of Xenophon,” Thomas Jefferson wrote to a friend in 1819, comparing Xenophon’s work favourably with the “mysticisms” and “whimsies” of Plato’s dialogues. More recently, many philosophers have taken the opposite view; a typical verdict is that of Terence Irwin in 1974, who described Xenophon as a “retired general” who presented “ordinary conversations.” The idea that Xenophon’s Socratic dialogues entirely lacked the philosophical bite or intellectual depth of Plato’s had become a commonplace in a philosophical discourse which prioritised abstract knowledge over broader ethics.
Both Jefferson and Irwin were right in identifying the characteristics of Xenophon’s depiction of his teacher—his overwhelming concern with providing practical advice for living a good life, and for managing relationships with family and friends. But both missed Xenophon’s lively wit, and his use of the dialogue form to put Socrates in conversation with Athenians, both friends and family and more public figures whose identity adds some spice to the discussion. Xenophon depicts a Socrates who offers pragmatic solutions to the difficulties his Athenian friends face, from Socrates’ own son’s rows with his mother to his friend Crito’s difficulties with vexatious lawsuits targeting his wealth. Where Plato shows Socrates leaving his conversation partners numbed and distressed by their recognition of their ignorance, as if attacked by a stingray, Xenophon takes more care to show how Socrates moved friends and students on from the discomfort of that initial learning moment. He offers practical solutions and friendly encouragement, whether persuading warring brothers to support each other or finding a way in which a friend can support the extended family taking refuge in his home. His advice is underpinned by an ethical commitment to creating and maintaining community.
It is not that Xenophon’s Socrates is afraid to show the over-confident the limits of their capabilities; while he offers encouragement and practical advice on personal and business matters, he rebukes those who want power and prestige without first doing their homework. His Socrates demonstrates to the young Glaucon that he needs to be much better informed about the facts and figures of Athenian civic and military resources before he proposes policy to his fellow citizens in Athens or seeks elected office. Socrates’ forensic uncovering of the young man’s ignorance of practical matters is sharpened for readers who recognise that this is Plato’s brother, depicted in his Republic as an acute interlocutor, able to follow Socrates’ most intellectually demanding arguments. In the conversation Xenophon presents, Glaucon is reduced to mumbling one excuse after another:
“Then first tell us,” said Socrates, “what the city’s land and naval forces are, and then those of our enemies.”
“Frankly,” he said, “I couldn’t tell you that just off the top of my head.”
“Well, if you have some notes of it, please fetch them,” said Socrates. “I would be really glad to hear what they say.”
“Frankly,” he said, “I haven’t yet made any notes either.”
(Memorabilia 3.6.9)
Xenophon might be making a very ordinary claim here, that good leadership decision-making rests on a firm grasp of practical detail. But it gains depth when read against Plato’s argument in the Republic for handing over political leadership to philosopher kings, trained in theoretical disciplines. Xenophon argues that rule should be grounded from the bottom up; he is a firm believer in transferable skills, and that the ability to manage a household might equip someone to lead an army or their city.
Xenophon does not leave Glaucon quite as discomfited as Socrates’ interlocutors in Platonic dialogues become, such as the Euthyphro where the titular character hurries away rather than go through another round of being disabused of his opinions. He shows how Socrates moves on from the low point of the realisation of ignorance and starts to rebuild his interlocutors’ self-confidence, now underpinned by knowledge and self-awareness. Socrates offers Glaucon a careful recommendation for developing his management skills and gaining credibility before returning to public debates as a more impressive contributor. With another student, Euthydemus, Socrates switches from the argumentative mode familiar from Plato’s work—the Socratic “elenchus” or refutation—to exhortation and encouragement, as teacher and student become more familiar with each other and learn together cooperatively.
“Responding to Plato’s dialogues with a less intellectualist account of the capacities that leaders need, Xenophon made a case for the importance of leadership skills and knowledge as the basis of public trust.”
One reason that Xenophon was motivated to show a Socrates who encouraged his students to make useful contributions to public life was to rebut critics who presented him—not entirely without cause—as the teacher of some of the leaders of the brutal regime of the Thirty, which briefly overthrew Athens’ democracy after the end of the Peloponnesian War. Xenophon insists that these former students had abandoned Socrates’ teaching in favour of an aggressive pursuit of power.
Xenophon recognised the usefulness of a wide range of practical experience. A businessman might well make a useful general. But he makes Socrates insist that leaders must show practical knowledge and analytical skills in order to persuade others to follow them and to deliver successful outcomes, whether in business or in battle. The combination of knowledge and skill, which his students label basilikē technē, the “royal art”,” is an essential attribute of leadership. By responding to Plato’s dialogues with a less intellectualist account of the capacities that leaders need, Xenophon made a case for the importance of leadership skills and knowledge as the basis of public trust. In a contemporary context where trust in leaders and educators alike is low, perhaps there is a powerful and accessible case for the role of expertise in government and society, which Xenophon makes through his memories of Socrates’ conversations.
Featured image: “The Death of Socrates” by Jacques-Louis David via The Met (public domain)
Women in the history of linguistics—from marginalization to recognition
Women’s History Month raises issues of erasure and marginalization, authority and power which, sadly, are still relevant for women today. Much can be learnt from the experience of women in the past. We find inspiring stories of women who overcame prejudice and constraints of all kinds and who sometimes managed to gain recognition from their peers, only to be excluded from the history of their discipline.
In the field of linguistics, this marginalization relates to some extent to what is today considered part of linguistics and the current valuing above all of theoretical work. Words matter: a broader definition of linguistics allows women across the centuries to be included in this scholarly field. Given the cultural and practical limitations imposed on their access to education across all cultures, we need to look outside more institutionalized and traditional frameworks to discover the contributions made by women to the study of language structure and function.
“Words matter: a broader definition of linguistics allows women across the centuries to be included in this scholarly field.”
Classic histories of linguistics, very rarely, if ever, include women scholars. We set about uncovering the contribution of women linguists—from European and non-European traditions— and their ideas and writings to give them the recognition they deserve. A group of equally motivated and determined scholars joined us in our quest. We looked for names, works and ideas, especially in those liminal spaces not reached by official historiography, that is, outside institutions, universities, and academies in more private and domesticated spaces. We decided to challenge categories and concepts devised for male-dominated accounts and expands our field of enquiry: we turned our attention not only to pioneers and exceptional women, but also to those non-exceptional women who nevertheless quietly moved forward our knowledge of languages, their description, analysis, codification and acquisition. Painstaking research in archives and libraries, looking at manuscripts and printed sources, gradually unearthed rich, fascinating, and often unexpected evidence of women’s contribution.
For the earlier periods, it was difficult to find women who published grammars or dictionaries, but they did exist. Marguerite Buffet in seventeenth-century France wrote a volume of observations on the good usage of French specifically aimed at women (Nouvelles observations sur la langue françoise, 1668). Similarly, in 1740, Johanna Corleva published a Dutch translation of Port-Royal’s celebrated general and rational grammar. In Portugal, in 1786, Francisca de Chantal Álvares produced a compendium of Portuguese grammar for female pupils in convent schools, the Breve Compendio da Gramatica Portugueza para uso das Meninas que se educaõ no Mosteiro da Vizitaçaõ de Lisboa, at a time when the majority of women did not have access to formal education. Further afield, women missionaries were also active in the field. Gertrud von Massenbach joined the Sudan Pioneer Mission in 1909, as a teacher of mathematics in Aswan, in Nubian territory. Her linguistic interests led her to publish a dictionary with a grammatical introduction of Kunûzi Nubian (Wörterbuch des nubischen Kunûzi-Dialektes mit einer grammatischen Einleitung, 1933) and a collection of Nubian texts (Nubische Texte im Dialekt der Kunuzi und der Dongolawi, 1962).
“We need to look outside more institutionalized and traditional frameworks to discover the contributions made by women to the study of language.”
But there is much more. Women were, for instance, the intended audience or dedicatees of some of the earlier vernacular grammars in Europe. The Gramática de la lengua castellana (1492) by Antonio de Nebrija, the very first printed grammar of a vernacular language in Europe, was commissioned by Queen Isabella I of Castile and, according to Juan de Valdés, was meant to be of benefit, “para las damas de la sereníssima doña Isabel” (“for the ladies-in-waiting of Her Very Serene Highness Queen Isabel”). Women were translators, language teachers, collectors of data on endangered languages, and creators of new scripts. In Jiangyong county (Jiāngyǒng xiàn) of Hunan (Húnán) province in China, a rural territory surrounded by mountains, the nǚshū script (“female script/writing”) was used and transmitted among village women for at least one and a half centuries: a variant of the Chinese script, it represents a significant example of Chinese women’s contribution to character invention and development.
Women also assisted male members of their families, or male colleagues, in their work as linguists. Lucy Catherine Lloyd (1834-1914), the sister-in-law of the German linguist Wilhelm Bleek, was his most important collaborator. Together they created the nineteenth-century archive of ǀXam and !Kung texts (today called the Digital Bleek and Lloyd), an invaluable resource for linguists working on Khoisan languages. Cinie Louw followed her husband Andrew Louw to South Rhodesia (today Zimbabwe) to work on the Morgenster Mission, learning the local language, Karanga, a Shona dialect, and becoming a fluent speaker. Their 1919 translation of the Bible into Karanga was a joint effort, preceded in 1915, by an important manual of the Chikaranga Language.
Other women’s linguistic work has been neglected or overshadowed, the men with whom they collaborated reaping the benefit of their efforts. The young Chiri Yukie (1903–1922) helped codify the oral tradition of the Ainu people of Hokkaido in northern Japan. Thanks to her bilingual and bicultural knowledge she was able to collect a wide range of oral performances, preserving them for posterity and making them accessible by translating them into Japanese. Her invaluable work ultimately ended up promoting, instead, the career of a prominent male academic who was awarded the Imperial prize for his work on the Indigenous language.
“Women’s personal and professional life cannot be separated in a way that has been possible for male scholars across the centuries.”
What came to light, piece by piece, through reading their personal stories, was the challenges women had to face in male-dominated academia. Women’s personal and professional life cannot be separated in a way that has been possible for male scholars across the centuries. Theirs are often tales of perseverance and determination. Take the example of Mary Haas, a stalwart of twentieth-century American Indian Linguistics and a central figure in the Boas-Sapir tradition, which laid the foundation for current language documentation practices. Haas found her marriage in 1931 to Morris Swadesh limited her opportunities both within linguistics and with respect to employment generally. Given the scarcity of academic appointments, she considered getting a teaching certificate to teach in public schools in Oklahoma to support herself and her fieldwork on Native American languages. However, as a married woman she was unlikely to get hired in a public school. Undeterred, she wrote to Swadesh asking for a divorce so that she might be able to support herself. Swadesh agreed. Their divorce was meant to allow Haas to pursue more avenues of employment, although her plans were ultimately interrupted by World War II.
Uncovering such stories proved complicated, but extremely rewarding. And the more we found, the more we have become convinced that there is still so much more to discover.
Read a free chapter from Women in the History of Linguistics on Oxford Academic.
Featured image from the cover of Women in the History of Linguistics by Wendy Ayres-Bennett and Helena Sanson.
Charity and solidarity! What responsibilities do nonprofits have towards Ukraine?
In a speech to the UN General Assembly in the fall of 2022, President Biden called on the UN to stand in solidarity with Ukraine. At least 1,000 companies have left Russia because of Putin’s brutal unprovoked war on Ukraine. Some companies left because of sanctions. Others left for moral reasons, often under pressure from investors, consumers, and out of empathy with their employees in Ukraine. But companies also have human rights responsibilities. Whether they stay or leave Russia will impact the war and human rights of the people of Ukraine. When companies leave en masse, Russia faces the possibility of economic oblivion.
Nonprofits can also impact the war. Russian oligarchs have donated lots of money to cultural organizations, universities, and think tanks, such as Harvard, MOMA, and the Council on Foreign Relations. Many of these donations are tainted by corruption and the close ties oligarchs have with Putin.
Philanthropy is a common way for oligarchs to launder their reputations, sometimes with an eye to future wrongdoing, what social psychologists call moral licensing. Studies show that people often follow their good acts with bad acts as a way to balance out the good with the bad. In the end, whatever good oligarchs do through their giving may be outweighed by the bad they’ve done in the past or will do in the future. But oligarchs are only part of the problem. Nonprofits that solicit and accept their donations are complicit in those harms, too.
What are the responsibilities of nonprofits? How should they meet their moral and human rights responsibilities during Russia’s war on Ukraine? What should we expect from museums, universities, and cultural organizations? If anything, they should be held to a higher standard than for-profit enterprises. After all, nonprofits serve the public good. They may not have had a physical presence in Russia, the way Starbucks and Levi Strauss did, but many of them are connected to Putin by way of Russian oligarchs.
“Philanthropy is a common way for oligarchs to launder their reputations, sometimes with an eye to future wrongdoing.”
Consider Viktor Vekselberg, a prominent Russian oligarch with close ties to Putin and head of the Skolkovo Foundation. Like many Russian oligarchs, he made his money with the collapse of the Soviet Union. The Skolkovo Foundation donated over $300 million to Massachusetts Institute of Technology (MIT) to support Skoltech, a program aimed at developing Russia’s tech sector. Vekselberg also sat on MIT’s Board of Trustees. It was only in 2018, after the US Treasury sanctioned him for “malign activities,” that MIT found the wherewithal to remove him from the Board. And, it was not until Russia invaded Ukraine that MIT ended the Skoltech Program, explaining, “this step is a rejection of the actions of the Russian government in Ukraine.” MIT finally got it right. Donors, such as Vekselberg, implicate nonprofits in Russia’s war on Ukraine. But had MIT done its due diligence from the outset, it would not have accepted Vekselberg’s donation in the first place. Boycotting oligarchs shows solidarity with the people of Ukraine, while doing nothing renders nonprofits complicit in the human rights violations suffered in Ukraine.
Vladimir Potanin, Russia’s richest oligarch, has supported the Kennedy Center and the Guggenheim Museum, among others. Until recently, he sat on the Board of Trustees at the Guggenheim, and on the Advisory Board of the Council of Foreign Relations. Potanin resigned from both in April 2022. Although not a Russian citizen, Len Blavatnik is a Russian insider who donated millions of dollars to Oxford, the Tate Modern, Yale, Harvard Medical School, and the Council of Foreign Affairs, to name a few of the elite recipients of his philanthropy. Aaron Ring, a Yale professor who received support from the Blavatnik Fund, called on Yale to suspend the Program. He was concerned that Yale was endorsing the donor. Yale maintained that since Blavatnik had not been sanctioned, his donation could be accepted. During Russia’s war on Ukraine, stakeholders like Aaron Ring don’t want to benefit from Russia’s oligarchs. They want to stand in solidarity with Ukraine.
The Guiding Principles on Business and Human Rights were endorsed by the UN Human Rights Council in 2011. They hold that enterprises are responsible not only for their direct human rights violations, but also for their indirect ones. So, what counts as an indirect violation in the nonprofit sector? When a nonprofit benefits from donors who are implicated in human rights violations, the nonprofit is complicit in the wrongs of the donors. Many Russian oligarchs are tied to Putin, have profited from their relationship with him, and stand to benefit from his war on Ukraine.
“Boycotting oligarchs shows solidarity with the people of Ukraine, while doing nothing renders nonprofits complicit in the human rights violations suffered in Ukraine.”
When nonprofits refuse to accept donations from oligarchs, they stand in solidarity with Ukraine against Russia. Given the tendency of oligarchs to donate to elite and high-profile organizations, boycotting them may create a bandwagon effect, or a little philanthropy warfare!
Russia has a long record of human rights violations. Freedom of expression is one. The Committee to Protect Journalists confirmed that 82 journalists and media workers were killed in Russia between 1992 and 2022. In 2020, Russia adopted a law banning so called “disrespect” to authorities. Its violation of the fundamental rights of LGBTQ people is longstanding. In 2013, it penalized so called “propaganda” about homosexuality. Activists and celebrities faced fines for supporting the LGBTQ community in Russia.
Now, Russia is under investigation for war crimes such as rape, torture, and execution style murders of civilians. As of 2022, the UN General Assembly resolved that Russia should withdraw its military forces from Ukraine. This came amidst reports of Russian attacks on residences, schools, hospitals, and on civilians, including women, people with disabilities, and children.
Have nonprofits done enough for human rights? No, not when it comes to Russian oligarchs. By laundering the reputations of oligarchs, nonprofits have enabled Putin’s war on Ukraine and the horrific suffering it has brought. The Guiding Principles can help nonprofits identify their human rights responsibilities and ensure that they are not complicit in Russia’s human rights violations. All enterprises should practice due diligence, a mechanism that prevents human rights violations and complicity in them. Refusing donations from Russian oligarchs is the very least nonprofits can do.
Transparency is at the heart of due diligence. Yale Professor Jeffrey Sonnenfeld has tracked which companies left Russia and which have stayed, providing much-needed transparency on the operations of for-profit enterprises. Not only does Sonnenfeld’s list incentivize companies to pull out of Russia, those that left have outperformed those that remained. Unfortunately, no such list exists for nonprofits. Tracking nonprofits with respect to Russian oligarchs, knowing and showing, would go a long way toward ensuring that they meet their human rights responsibilities.
To be sure, there is a risk that nonprofits will receive less money if they boycott Russian oligarchs. But it is also possible that they will be rewarded for doing the right thing, as Hands on Hartford was when it refused donations from the Proud Boys, a white supremacist group. Generous donors may come forward when they learn that nonprofits stand in solidarity with Ukraine. Granted, the impact nonprofits can have on the war in Ukraine is not as great as for-profit companies, if only because of scale. But keep in mind that nonprofits serve the public good, which if anything enhances their human rights responsibilities. In the long run, when nonprofits stand in solidarity with Ukraine, they serve the public good.
Featured image by Elena Mozhilov via Unsplash (publish domain)
A Q&A with Bryan Garner, “the least stuffy grammarian around”
The fifth edition of Garner’s Modern English Usage has recently been published by OUP. I was happy to talk to Bryan Garner—who has been called “the least stuffy grammarian around” and was declared a “genius” by the late David Foster Wallace—about what it means to write a usage dictionary.
“Possesses” is a good word for it. In my case, it was matter of falling in love with the genre as a teenager. I discovered Eric Partridge’s Usage and Abusage (1942) and immediately felt that it was the most fascinating book I’d ever held. Partridge discussed every “problem point” in the language—words that people use imprecisely, phrases that professional editors habitually eliminate, words that get misspelled because people falsely associate them with similar-looking words, the common grammatical blunders, and so on. And then Partridge had essays on such linguistic topics as concessive clauses, conditional clauses, elegancies, hyphenation, negation, nicknames, and obscurity (“It may be better to be clear than clever; it is still better to be clear and correct.”).
At the age of 16, I was going on a ski trip with friends, and the book had just arrived in the mail as I was leaving for New Mexico. I stuck it in my bag and didn’t open it until we arrived at the ski lodge. Upon starting to read it, I was hooked. In fact, I didn’t even ski the first day: I was soaking up all that I could from Usage and Abusage, which kept mentioning some mysterious man named Fowler.
So when I got home, I ordered Fowler’s Modern English Usage (2d ed. 1965), and when it arrived I decided it was even better. By the time I was 17, I’d memorized virtually every linguistic stance taken by Partridge and Fowler, and I was thoroughly imbued with their approach to language. By the time I’d graduated from high school, I added Wilson Follett, Bergen Evans, and Theodore Bernstein to the mix. I was steeped in English usage—as a kind of closet study. I spent far more time on these books than I did on my schoolwork.
I suppose in retrospect it looks predictable that I’d end up writing a usage dictionary. I started my first one (A Dictionary of Modern Legal Usage) when I was 23, and I’ve been at it ever since. That was 41 years ago, and it ended up being my first book with Oxford University Press.
You’re asking me to psychoanalyze myself? Okay, it’s true. When I was four, in 1962, my grandfather used Webster’s Second New International Dictionary as my booster seat. I started wondering what was in that big book.
Then, in 1974, when I was 15, one of the most important events of my life took place. A pretty girl in my neighborhood, Eloise, said to me, with big eyes and a smile: “You know, you have a really big vocabulary.” I had used the word facetious, and that prompted her comment.
It was a life-changing moment. I would never be the same.
I decided, quite consciously (though misguidedly), that if a big vocabulary impressed girls, I could excel at it as nobody ever had. By that time, my grandparents had given me Webster’s Second New International Dictionary, which for years had sat on a shelf in my room. I took it down and started scouring the pages for interesting, genuinely useful words. I didn’t want obsolete words. I wanted serviceable words and remarkable words. I resolved to copy out, by hand, 30 good ones per day—and to do it without fail.
“I decided, quite consciously (though misguidedly), that if a big vocabulary impressed girls, I could excel at it as nobody ever had.”
I soon discovered I liked angular, brittle words, such as cantankerous, impecunious, rebuke, and straitlaced. I liked aw-shucks, down-home words, such as bumpkin, chatterbox, horselaugh, and mumbo-jumbo. I liked combustible, raucous words, such as blast, bray, fulminate, and thunder. I liked arch, high-toned words, such as athwart, calumny, cynosure, and decrepitude. I liked toga-wearing, Socratic-sounding words, such as eristic, homunculus, palimpsest, and theologaster. I liked mellifluous, polysyllabic words, such as antediluvian, postprandial, protuberance, and undulation. I liked the technical and quasi-technical terms of rhetoric, such as asyndeton, periphrasis, quodlibet, and synecdoche. I liked frequentative verbs with an onomatopoetic feel, such as gurgle, jostle, piffle, and topple. I liked evocative words about language, such as billingsgate, logolatry, wordmonger, and zinger. I liked scatological, I-can’t-believe-this-term-exists words, such as coprolalia, fimicolous, scatomancy, and stercoraceous. I liked astonishing, denotatively necessary words that more people ought to know, such as mumpsimus and ultracrepidarian. I liked censoriously yelping words, such as balderdash, hooey, pishposh, and poppycock. I liked mirthful, tittering words, such as cowlick, flapdoodle, horsefeathers, and icky.
In short, I fell in love with language. I filled hundreds of pages in my vocabulary notebooks.
In the end, I decided that I liked the word lexicographer better than copyist, so I tried my hand at it.
I was trying to impress her, it’s true. I never called her. I just started using lots of big words. It took me about two years to realize that big words, in themselves, have no intrinsic value in attracting females. Perhaps the opposite.
But that’s okay. By the time I was 17, I had this prodigious vocabulary. I thought of SAT words as being quite elementary. I had a larger vocabulary then than I do today. You can see why, at the ski lodge in early 1975, this particular teenager was absolutely primed to relish the work of Eric Partridge and H.W. Fowler.
That’s true. But it all began with words and English usage. Then I moved to legal lexicography and other language-related topics.
Many if not most lexicographers today are interested in slang, in current catchphrases, and in jargon—the more shifting and volatile parts of language. (Always something new!) I’m different. I’ve always been interested in the durable parts. In my usage book, I tackle the difficult question of what, precisely, constitutes Standard Written English. In any era, that’s a complicated question or series of questions. And so I’ve answered it in a 1,200-page book, word by word and phrase by phrase. It’s intended for writers, editors, and serious word lovers.
Of course. I’m very Fowlerian and Partridgean in my mindset. Though all my essays are original, some bear the same category-titles as Fowler’s (for example, “Archaisms,” “Needless Variants,” and “Split Infinitives” ) or Partridge’s (“Clichés,” “Johnsonese,” and “Slang” [yes, that]). Meanwhile, I’ve created new essay-categories of my own, much in the mold of my admired predecessors: “Airlinese,” “Estranged Siblings,” “Hypercorrection,” “Irregular Verbs,” “Skunked Terms,” “Word-Swapping,” and the like). I have a dozen new essays in the fifth edition, including “Irreversible Binomials,” “Loanwords,” “Prejudiced and Prejudicial Terms,” “Race-Related Terms,” and “Serial Comma” (a big one). These essays are some fun.
Yes, something like that. Consider an example. Note that an asterisk before a term denotes that it’s nonstandard:
tic-tac-toe (the elementary game in which two players draw X’s or O’s within a pattern of nine squares, the object being to get three in a row), a phrase dating from the mid-1800s in AmE, has been predominantly so spelled since about 1965. Before that, the variants *tick-tack-toe, *ticktacktoe, and even *tit-tat-toe were about equally common. The British usually call the game noughts and crosses.
Current ratio (tic-tac-toe vs. *tit-tat-toe vs. *tick-tack-toe vs. *ticktacktoe): 96:4:3:1
There are thousands of such entries. As you can see, a usage-book entry is entirely different from a normal dictionary entry.
Yes. Those are key. I’m capitalizing on big data, which makes GMEU entries empirically grounded in a way that earlier usage books couldn’t be. This is a great era for lexicographers and grammarians: we can assess word frequencies in various databases that include millions of published and spoken instances of a word or phrase. By comparison, the evidence on which Fowler and Partridge based their opinions was sparse. In my case, opinion is kept to a minimum, and facts come to the fore. Sometimes that entails inconveniently discovering that the received wisdom has been way off base.
“People who say they’re sticking to the original Fowler might as well be driving an original Model-T.”
I’ve heard that. It’s a naive view. For one thing, the empirical statistics on relative word frequencies have been updated from 2008 to 2019. The language has evolved: email is now predominantly solid. There are thousands of updated ratios, and some of the judgments differ from those in past editions. For example, overly and snuck are now declared to be unobjectionable.
Every single page of the book has new material. It’s a big improvement. The six years have allowed for much more research.
People who say they’re sticking to the original Fowler might as well be driving an original Model-T.
Here’s something reference books have in common with medical devices. There’s no reason for a new one unless it’s a significant improvement over its precursors. That’s how the field gets better and better.
I’m just a dogged researcher. That’s all. Research is simply formalized curiosity, and I seem to have an inexhaustible curiosity about practical problems that arise for writers and editors. I certainly wouldn’t call myself “the world’s leading authority on the English language.”
I’ve also been helped by generous scholars, especially by John Simpson, the Oxford lexicographer, and Geoffrey K. Pullum, the Edinburgh grammarian. And then I had a panel of 34 critical readers who minutely reviewed 55-page segments for suggested improvements. I can’t tell you how grateful I am for the contributions of all these erudite friends.
In any event, a lexicographer must be especially adept at delayed gratification. You labor for years and then wait. You’re lucky, as Samuel Johnson once said, if you can just “escape censure.” That some people have praised my work, after all these years of toil, is certainly pleasing. But for me, the real pleasure is in the toil itself: asking pertinent questions and finding useful, fact-based answers to all the nettlesome problems that arise in our wildly variegated English language.
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