Bibliographic record
Abstract
Citizens of industrialized countries largely share a sense that national and international governance is inadequate, believing not only that public authorities are incapable of making the right policy decisions, but also that the entire network of state and civil society actors responsible for the discussion, negotiation, and implementation of policy choices is untrustworthy. Using agro-environmental policy development in France, the United States, and Canada as case studies, Éric Montpetit sets out to investigate the validity of this distrust through careful attention to the performance of the relevant policy networks. He concludes that distrust in policy networks is, for the most part, misplaced because high levels of performance by policy networks are more common than many political analysts and citizens expect. Opposing the tenets of state retrenchment, his study reveals that providing participation in governance to resourceful interest groups and strong government bureaucracies is an essential component of sound environmental policies for agriculture. A timely and crucial contribution to the good governance debate, this book should be required reading for policy makers and politicians, as well as students and scholars of public policy, political science, environmental studies, and government.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".