Bibliographic record
Abstract
Municipal image-building now promotes cities globally, and also to their own citizens. Image-building in Canadian Municipalities explores the decision making processes that determine how cities and towns choose to represent themselves. It also assesses the effectiveness of those processes and of the images themselves. Documenting how image-building policies vary across municipalities and provinces, contributors focus on the interaction between various levels of government and on the involvement and influence of business organizations, heritage associations, environmental groups, and other social forces. Delving into largely unexplored areas of research, with a particular interest in smaller towns and cities, authors show how municipal image-making is often used to advance other policy objectives, and thereby intersects with areas such as culture, economic development, tourism, and immigration. Image-building in Canadian Municipalities shows how municipalities of all sizes are conscious of their images. Thought-provoking and instructive, it provides lessons to policy makers and social interest groups about creating better public policies. Contributors include Caroline Andrew (University of Ottawa), John C. Lehr (University of Winnipeg), Judy Lynn Richards (University of Prince Edward Island), Cristine de Clercy (University of Western Ontario), Peter Ferguson (University of Western Ontario), and Karla Zubrycki International Institute for Sustainability, Winnipeg).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".