Bibliographic record
Abstract
Overarching AimThis book documents the motivations, land-use effects, and financial implications of municipal boundary adjustments across Canada, focusing mainly on annexations and amalgamations-the most frequent means to adjust boundaries and reform local governments in this country.Explored among several provinces across Canada, these two common policy tools provide empirical information from which to develop generalizations and inform best practices for municipal boundary adjustments and reform.Nevertheless, other forms of municipal boundary adjustment have been undertaken in almost all provinces and territories along with these two tools within larger contexts.Given this complex terrain, this volume aims to uncover hidden motivations, untangle behind-the-scenes political machinations, and document the ensuing "battles," with a focus on mid-size cities and small towns away from major Canadian metropolitan areas such as Vancouver, Toronto, or Montreal, and in provinces other than Ontario and Quebec.The collection traverses new ground through its deployment of empirical evidence, case studies, and examples to explain the phenomenon of municipal boundary adjustment and how the esoteric aspects of boundary adjustments work in more practical applications.Municipal boundary adjustment, as the term suggests, refers to altering the legal boundaries of a municipality.Adjusting municipal boundaries significantly affects a municipality, informing how and where it might grow, contributing to the management of its financial affairs, and providing services to its
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.307 | 0.139 |
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".