Bibliographic record
Abstract
<JATS1:p>Whether striving to protect citizens from financial risks, climate change, inadequate health care, or the uncertainties of the emerging “sharing” economy, regulators must routinely make difficult judgment calls in an effort to meet the conflicting demands that society places on them.</JATS1:p> <JATS1:p>Operating within a political climate of competing demands, regulators need a lodestar to help them define and evaluate success.Achieving Regulatory Excellenceprovides that direction by offering new insights from law, public administration, political science, sociology, and policy sciences on what regulators need to do to improve their performance.</JATS1:p> <JATS1:p>Achieving Regulatory Excellenceoffers guidance from leading international experts about how regulators can set appropriate priorities and make sound, evidence-based decisions through processes that are transparent and participatory. With increasing demands for smarter but leaner government, the need for sound regulatory capacity—for regulatory excellence—has never been stronger.</JATS1:p> <JATS1:p>In addition to chapters by editor Cary Coglianese, and a foreword by Jim Ellis, president and chief executive officer of the Alberta Energy Regulator, contributors include Robert Baldwin (London School of Economics and Political Science), John Braithwaite (Australian National University), Angus Corbett (University of Pennsylvania), Daniel Esty (Yale University), Adam Finkel (University of Pennsylvania and University of Michigan), Ted Gayer (Brookings Institution), John Graham (Indiana University), Neil Gunningham (Australian National University), Kathryn Harrison (University of British Columbia), Bridget Hutter (London School of Economics and Political Science), Howard Kunreuther (Wharton School at the University of Pennsylvania), David Levi-Faur (Hebrew University of Jerusalem), Shelley H. Metzenbaum (Volcker Alliance), Donald P. Moynihan (University of Wisconsin–Madison), Paul R. Noe (American Forest and Paper Association), Gaurav Vasisht (Volcker Alliance), David Vogel (University of California–Berkeley), and Wendy Wagner (University of Texas School of Law).</JATS1:p>
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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 | 0.094 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.039 | 0.025 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.032 | 0.035 |
| Insufficient payload (model declined to judge) | 0.058 | 0.060 |
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".