Bibliographic record
Abstract
This chapter introduces the concept of Temporal Focal Points in explaining change in international institutions. In doing so, it elaborates theoretically the arguments contained in the introductory chapter. It models the coordination challenge facing states as a stag hunt game, where international actors can all benefit if they are able to cooperate. In hunting stag – or, engaging in institutional change efforts – actors can move to a Pareto-superior, payoff-dominant equilibrium. The challenge that they face is that if they hunt stag and others do not, they will expend scarce assets and end up significantly worse off. The risk of acting cooperatively when others do not leads actors to persist at inferior, risk-dominant equilibria. This can change suddenly, however, when actors reach a temporal convergence of expectations. The convergence is often facilitated by the arrival of Temporal Focal Points. The heightened probability of successful coordination leads to sharp increases in political and analytical investments. The chapter concludes with a discussion of methodology and case selection.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".