making our models explicit

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We owe it to ourselves to develop an explicit model of any situation that concerns us. Whether we choose to share our model with other people is a choice, but we should always be ready to describe it to ourselves.

A good model simplifies reality in a way that enables wise judgments. It should include the most significant components of the situation and link them together. It will certainly not be complete or conclusive; in fact, an important reason to make our model explicit is to help think about what it may omit, what it assumes without sufficient evidence, how it may appear wrong to people who’ve had different experiences, and how it would change if the world changed.

In our Introduction to Civic Studies course so far this fall, we have already explored various models. For instance, Elinor Ostrom and her colleagues developed a template for modeling almost any institution, the Institutional Analysis and Design (IAD) framework. I discuss that framework in this 11-minute recorded lecture. (I also offer an introductory lecture about Ostrom). Later, we examined the social theory of Jürgen Habermas, which I presented as a model involving the Lifeworld, Public Sphere, and Systems in this 29-minute lecture. Both examples can be represented in the form of flow charts. But we also unpacked Robert Putnam’s model of social capital, in which the main construct–composed of numerous elements–causes good social outcomes (see this 18-minute lecture). And we discussed the mental model that guided the leaders of the Montgomery Bus Boycott when they chose to target the city’s bus company.

As these examples suggest, models can come in many forms. They need not be presented visually; Putnam’s model is an equation, and narratives also work. (We read a little of Pierre Bourdieu, who presented a model in the form of an emblematic story.) The components can be many kinds of things, from actors to psychological constructs to ideals. And the connections can be equally various. One component may cause or influence another, or it may exemplify, help to compose, encompass, intend, ground, block, explain, or evolve into another part of the model.

I would go so far as to describe making and critically assessing one’s own models as a fundamental civic skill.

See also: two models for analyzing policy; why social scientists should pay attention to metaphysics; individuals in cultures: the concept of an idiodictuon

call for papers: special issue on civically engaged research in political science

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The journal Politics, Groups, and Identities has issued a call for papers on “How to Conduct Civically Engaged Research in a Time of Contentious Politics.” The editors for this special issue, Shelly Arsneault, Angie Bautista-Chavez, Stephanie Chan, and Valerie Martinez-Ebers, were participants in last summer’s Institute for Civically Engaged Research (ICER), which is a project of the American Political Science Association and Tisch College at Tufts. Several participants formed the idea of a special issue during a breakout group during ICER, which makes it an example of a productive small-group activity! Their call for papers follows, and the link for submissions is here:

https://think.taylorandfrancis.com/special_issues/conduct-civically-engaged-research-contentious-politics/

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In 2021, PS: Political Science & Politics published a collection of articles that sought to define and, ultimately, motivate political scientists to conduct civically engaged research (see Dobbs, Hess, Bullock, and Udani 2021). This special issue builds on these efforts by providing a guide for how to conduct ethical and rigorous civically engaged research. The collection of manuscripts will address the ethics, research design, methodology, and project management involved in developing, implementing, and communicating results from civically engaged research. By providing examples of successful civic engagement research and practical scaffolds, the Special Issue will serve as a valuable guide for political scientists as they develop their civically engaged research projects, while collectively advancing theoretical debates, ethical practices, and methodological pluralism in the discipline of political science. 

We invite scholars across all political science subfields using a range of methodologies to submit manuscripts addressing the following or related questions.

  • How is civically engaged research distinct from other methodologies in political science?
  • How can civically engaged research inform canonical theories of power, politics, and governance?
  • How to define “community” when conducting civically engaged research?
  • How can political science research center community expertise?

We are especially interested in examples and guides that range across research sites and across a range of issue areas.

  • Examples of civically engaged research in challenging or hostile political environments.
  • Examples of civically engaged research working with vulnerable communities.
  • Examples of civically engaged research that showcase variation in methods, data analysis, and data collection.

Sappho 31

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That guy       a god
who sits       near you
Your voice     your eyes
For him

My heart       it stops
My tongue      it's stuck
To watch       you there
with him

I sweat        I'm cold
I shake        I'm pale
I'm grass      that's bleached
I'm stunned

My lips        won't move
My ears        hear buzz
I spark        lit up
I'm done

This poem by Sappho, which survives in the fragment beginning phainetai moi (“it seems to me”), may be the best known and most often translated lyric from ancient Greece or Rome. Here are 43 translations, offering diverse responses to Sappho’s lines and illustrating the evolution of English since the 1500s.

I tried a compressed translation, with no adverbs, no adjectives as modifiers (only predicates), and the fewest words possible. I chose 30 iambs to stand for Sappho’s 202 syllables. I consulted the Greek text but had many difficulties with the dialect (Aeolic), so I leaned on previous translations. This is like an amateur’s sketch of a famous painting, merely recording the outlines.

I agree with readers who see three persons here: the narrator, a man, and the “you” who is giving attention to that man. If the narrator is Sappho (or has her gender) then the poem is spoken by a woman who loves “you,” and you could be a second woman. However, the genders of the narrator and the beloved are never specified and can be imagined differently.

In a somewhat less compressed version, the man mentioned at the outset would not be a god. The text says that he seems similar to a god, and the point may be that his situation is divinely fortunate. The narrator is paler green than grass; and a thin or delicate signal fire flows through her. (I can’t help thinking of an electrical charge.) At the end, she says it seems she’s nearly dead, the verb “to seem” echoing the first line.

But that wasn’t the end of the original poem. This is all we have of the remaining stanzas:

But things      go on 
[…]             The poor        
[…]

One of many debated points is whether the narrator is jealous. I doubt it. She (?) focuses on and talks to the other person, and perhaps neither of them cares much about the man. Hence my somewhat dismissive opening (“That guy …”).

Another good question is what Sappho wrote after the last words that survive: “But all is to be endured, and the poor man/person …” Our text ends there because this poem only survived as a quotation in Longinus’ On the Sublime, and Longinus left off in mid-thought. Although I blame him for the lost strophes, I also find this a moving place to stop. Things must go on; we know that. But how did Sappho actually go on? And what did she say about “the poor”?

See also: when you know, but cannot feel, beauty (on “The Ode to a Nightingale,” which is influenced by Sappho 31); “The Wedding of Peleus and Thetis,” the sublime and other people, and “Madonna è disiata in sommo cielo.”

the ACM brief on AI

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The Association for Computing Machinery (ACM) has 110,000 members. As artificial intelligence rapidly acquires users and uses, some ACM members see an analogy to nuclear physics in the 1940s. Their profession is responsible for technological developments that can do considerable good but that also pose grave dangers. Like physicists in the era of Einstein and Oppenheimer, computer scientists have developed ideas that are now in the hands of governments and companies that they cannot control.

The ACM’s Technology Policy Council has published a brief by David Leslie and Francesca Rossi with the following problem statement: “The rapid commercialization of generative AI (GenAI) poses multiple large-scale risks to individuals, society, and the planet that require a rapid, internationally coordinated response to mitigate.”

Considering that this brief is only three pages long (plus notes), I think it offers a good statement of the issue. It is vague about solutions, but that may be inevitable for this type of document. The question is what should happen next.

One rule-of-thumb is that legislatures won’t act on demands (let alone friendly suggestions) unless someone asks them to adopt specific legislation. In general, legislators lack the time, expertise, and degrees of freedom necessary to develop responses to the huge range of issues that come before them.

This passage from the brief is an example of a first step, but it won’t generate legislation without a lot more elaboration:

Policymakers confronting this range of risks face complex challenges. AI law and policy thus should incorporate end-to-end governance approaches that address risks comprehensively and “by design.” Specifically, they must address how to govern the multiphase character of GenAI systems and the foundation models used to construct them. For instance, liability and accountability for lawfully acquiring and using initial training data should be a focus of regulations tailored to the FM training phase.

The last quoted sentence begins to move in the right direction, but which policymakers should change which laws about which kinds of liability for whom?

The brief repeatedly calls on “policymakers” to act. I am guessing the authors mean governmental policymakers: legislators, regulators, and judges. Indeed, governmental action is warranted. But governments are best seen as complex assemblages of institutions and actors that are in the midst of other social processes, not as the prime movers. For instance, each legislator is influenced by a different set of constituents, donors, movements, and information. If a whole legislature manages to pass a law (which requires coordination), the new legislation will affect constituents, but only to a limited extent. And the degree to which the law is effective will depend on the behavior of many other actors inside of government who are responsible for implementation and enforcement and who have interests of their own.

This means that “the government” is not a potential target for demands: specific governmental actors are. And they are not always the most promising targets, because sometimes they are highly constrained by other parties.

In turn, the ACM is a complex entity–reputed to be quite decentralized and democratic. If I were an ACM member, I would ask: What should policymakers do about AI? But that would only be one question. I would also ask: What should the ACM do to influence various policymakers and other leaders, institutions, and the public? What should my committee or subgroup within ACM do to influence the ACM? And: which groups should I be part of?

In advocating a role for the ACM, it would be worth canvassing its assets: 110,000 expert members who are employed in industry, academia, and governments; 76 years of work so far; structures for studying issues and taking action. It would also be worth canvassing deficits. For instance, the ACM may not have deep expertise on some matters, such as politics, culture, social ethics, and economics. And it may lack credibility with the diverse grassroots constituencies and interest-groups that should be considered and consulted. Thus an additional question is: Who should be working on the social impact of AI, and how should these activists be configured?

I welcome the brief by David Leslie and Francesca Rossi and wouldn’t expect a three-page document to accomplish more than it does. But I hope it is just a start.

See also: can AI help governments and corporations identify political opponents?; the design choice to make ChatGPT sound like a human; what I would advise students about ChatGPT; the major shift in climate strategy (also about governments as midstream actors).

can AI help governments and corporations identify political opponents?

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In “Large Language Model Soft Ideologization via AI-Self-Consciousness,” Xiaotian Zhou, Qian Wang, Xiaofeng Wang, Haixu Tang, and Xiaozhong Liu use ChatGPT to identify the signature of “three distinct and influential ideologies: “’Trumplism’ (entwined with US politics), ‘BLM (Black Lives Matter)’ (a prominent social movement), and ‘China-US harmonious co-existence is of great significance’ (propaganda from the Chinese Communist Party).” They unpack each of these ideologies as a connected network of thousands of specific topics, each one having a positive or negative valence. For instance, someone who endorses the Chinese government’s line may mention US-China relationships and the Nixon-Mao summit as a pair of linked positive ideas.

The authors raise the concern that this method would be a cheap way to predict the ideological leanings of millions of individuals, whether or not they choose to express their core ideas. A government or company that wanted to keep an eye on potential opponents wouldn’t have to search social media for explicit references to their issues of concern. It could infer an oppositional stance from the pattern of topics that the individuals choose to mention.

I saw this article because the authors cite my piece, “Mapping ideologies as networks of ideas,” Journal of Political Ideologies (2022): 1-28. (Google Scholar notified me of the reference.) Along with many others, I am developing methods for analyzing people’s political views as belief-networks.

I have a benign motivation: I take seriously how people explicitly articulate and connect their own ideas and seek to reveal the highly heterogeneous ways that we reason. I am critical of methods that reduce people’s views to widely shared, unconscious psychological factors.

However, I can see that a similar method could be exploited to identify individuals as targets for surveillance and discrimination. Whereas I am interested in the whole of an individual’s stated belief-network, a powerful government or company might use the same data to infer whether a person would endorse an idea that it finds threatening, such as support for unions or affinity for a foreign country. If the individual chose to keep that particular idea private, the company or government could still infer it and take punitive action.

I’m pretty confident that my technical acumen is so limited that I will never contribute to effective monitoring. If I have anything to contribute, it’s in the domain of political theory. But this is something–yet another thing–to worry about.

See also: Mapping Ideologies as Networks of Ideas (talk); Mapping Ideologies as Networks of Ideas (paper); what if people’s political opinions are very heterogeneous?; how intuitions relate to reasons: a social approach; the difference between human and artificial intelligence: relationships