Collective Discussion: Diagnosing the Present
Bibliographic record
Abstract
As students and scholars of global politics, we have been witnessing, participating in, and feeling the effects of recent global upheavals. These include specific events, such as the election of Trump and the vote for Brexit, but are better understood through their resulting political effects (e.g., pushing back on migration, hardening national borders, denying climate change, reneging on trade deals, gutting the welfare state, increasing resource extraction, and curtailing rights). Commentators refer to these upheavals in different ways: a rise in populism, reinvigorated nationalism, the new fascism, a polarization of Right and Left, the end of globalization, and posttruth politics. These labels have not only generated a great deal of scholarly debate, they have also helped generate multiple energies, including activism, protest, and politicization. Such developments feel at once totally unprecedented but also eerily familiar. More to the point, they have very different manifestations in different parts of the world; indeed, one of the difficulties of the present moment is the lack of analysis about the global ramifications of these upheavals. We want to intervene in this puzzle by tapping into an underlying anxiety about how we understand what is currently happening in the world. How do we talk about these present formations without lapsing into nostalgia, blind panic, or unhelpful predictions? This is not an exercise in mastery—as if that is even possible—but more a reflection on the difficulties of speaking about this strange contemporary collusion of power, disenfranchisement, and violence. In keeping with the open-ended ethos that characterizes International Political Sociology, we are interested in how to diagnose these forces in ways that do not singularize, homogenize, or reduce them to something that can be solved once and for all.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.037 | 0.072 |
| Scholarly communication | 0.033 | 0.039 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.022 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".