POLICYMAKING IN A GLOBAL CONTEXT: THE BENEFITS OF SELF-INTEREST AND INDEPENDENT ACTION
Bibliographic record
Abstract
* The views expressed in this paper are those of the author. No responsibility for them should be attributed to the Bank of Canada. The author would like to thank his colleagues for their many helpful suggestions and acknowledge the valuable assistance of Suzanne Le Blanc. Policymaking in a Global Context: The Benefits of Self-Interest and Independent Action Interest in international policy coordination seems to be on the ascendency. A number of working papers have appeared in the last few months, reprising the literature of the late 1980s when this topic last came to the fore. Several high profile conferences (such as the present one) have also been organized. The reasons for this renewed interest are not hard to understand, and have both a practical and a theoretical dimension. The attraction for policymakers reflects a growing concern about the number of shocks that have recently hit the world economy, and a sense that coordinated international action might be more effective in dealing with the consequences. Although academics have also been drawn to this area by recent real world
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".