Bibliographic record
Abstract
This collection about public policy making in the major cities of Canada is long overdue.We are bombarded with messages about the growing importance of cities for national and regional economies, and we have considerable scholarly information about municipal government.But there is not much material available about how the policies that prevail in urban spaces, many of which are not generated by municipalities, actually come about.This is the gap addressed by this book.All of the authors look at concrete policies and analyze them as a function of two major determinants.The irst is the complex set of interactions between interested oficials and politicians from the federal, provincial, and municipal levels of government.The second is the dense array of organized interests, or "social forces," that articulate various preferences and are involved to various degrees in policy-making processes.Of course the authors are not conined to this explanatory mission.They understand their cities thoroughly, and they provide ine-grained portraits of their essential character, built on sketches of their history, economy, demography, and political culture, so that the research will be of interest not only to scholars, students, and policy makers but also, we very much hope, to citizens.We also hope that suggestions for improving policy and the policy-making process will bear fruit in improved conditions for citizens.This collection presents original research that stands alone and makes a signiicant contribution to our understanding of public policy in cities.But the work is also part of a larger project, Multilevel Governance and Public Policy in Canadian Municipalities.As is explained in the Introduction to this volume, the project has several components.Some involve smaller Canadian municipalities, but all focus on the six policy
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 | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.625 | 0.470 |
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".