Bibliographic record
Abstract
This is a collection of papers about how municipalities in Canada construct and project their images.This is a fascinating field for those interested in municipal government, and also for those concerned with public policy.The authors here treat image-building as a policy and focus on how images are constructed.They investigate two main determinants of policy.The first is the set of relationships between governmental actors at the municipal, provincial, and federal levels; that is, the sets of intergovernmental relations that help produce the policies.The second is the array of "social forces" (more or less organized interests of all kinds) that are concerned about the image of their municipality, and that aim to influence image-building policy.Global forces have brought image-building to the fore in many municipalities.Localities face challenges of economic restructuring and are often competing for incoming investments and immigrants.The Internet has made it possible for all municipalities, even very small ones, to project themselves on a regional, national, and global scale.This book covers some large cities, but it also explores imagebuilding in small and medium-sized towns, and the chapters in this book clearly show that municipalities of all sizes are conscious of their images and devote attention to optimizing them.This collection presents original research by expert scholars that stands alone and makes a significant contribution to our knowledge about image-building in Canadian municipalities.But the work collected here is also part of a much larger project, one that explores multilevel governance and public policy in Canadian municipalities.This project has many components, but most of the work has been done on six policy areas, one of which is image-building.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.427 | 0.186 |
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".