Bibliographic record
Abstract
Introduction Why did the Cold War end peacefully, without a shot being fired? Why did some European democracies survive the interwar period while others were replaced by fascist dictatorships? In the post-Cold War world, civil conflicts have replaced interstate war as the dominant form of organized political violence, with rebel groups – instead of intercontinental ballistic missiles (ICBMs) – as a key focus of both policy and scholarship. Yet what makes such groups tick? Why do some engage in wanton killing and sexual violence while others do not? The European Union is a unique experiment in governance “beyond the nation state,” but how are its supranational governance structures being crafted and with what effect on the ordinary citizens of Europe? Contemporary political science has converged on the view that these puzzles, and many more on the scholarly and policy agendas, demand answers that combine social and institutional structure and context with individual agency and decision-making. This view, together with recent developments in the philosophy of science, has led to an increasing emphasis on causal explanation via reference to hypothesized causal mechanisms. Yet this development begs the questions of how to define such mechanisms, how to measure them in action, and how to test competing explanations that invoke different mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 0.021 |
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".