Bibliographic record
Abstract
Policy making in the modern world has become a complex matter.Much policy is formed through negotiations between governments at several different levels, because each has particular resources that can be brought to bear on problems.At the same time, non-governmental organizations make demands about policy and can help in policy formation and implementation.In this context, works in this series explore how policy is made within municipalities through processes of intergovernmental relations and with the involvement of social forces of all kinds.The Fields of Governance series arises from a large research project, funded mainly by the Social Sciences and Humanities Research Council of Canada, entitled Multilevel Governance and Public Policy in Canadian Municipalities.This project has involved more than eighty scholars and a large number of student assistants.At its core are studies of several policy fields, each of which was examined in a variety of municipalities.Our objectives are not only to account for the nature of the policies but also to assess their quality and to suggest improvements in policy and in the policy-making process.The Fields of Governance series is designed for scholars, practitioners, and interested readers from many backgrounds and places.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.927 | 0.904 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".