Bibliographic record
Abstract
Change of status 10 3.3 Amalgamation 2 0.7 Incorporation 2 0.7 Total 302 100.0 Note: Percent totals may not equal 100 due to rounding.Source: Statistics Canada (2021).Historically, municipalities of all types (e.g., town, city, rural municipality, and so forth) have participated in annexation.Most often, annexation reflects a growing urban community acquiring land from an adjacent rural municipality.Geographically, approximately 25% of all annexations in the dataset occurred within the general vicinity of Saskatchewan's two largest cities, Saskatoon and Regina, with a nearly even split between the two.The majority of annexations, over 70%, occurred in rural communities with a population, generally, of 10,000 people or less. 2 The existing body of literature covering annexation is relatively thin com-to the extensive catalogue of planning-related research, and what does exist is largely American focused.Edwards (2011) details several historical and contemporary motivations for municipal annexation in an American context.Reasons include offsetting the fiscal implications of residents leaving central cities for the fringe, capturing new tax revenues, providing for growth in an orderly manner complete with good land-use planning principles, preventing fragmentation of metropolitan areas, providing urban services to rural areas, and annexing for political and racial motivations.In Canada, Agrawal et al. (2021) found that the motivations for annexation are generally related to the goal of growth, as they include some of the following reasons: expanding and diversifying municipal revenues, anticipating largescale development, banking land, creating strategic buffers, and enabling joint planning and cooperation.This is consistent with Meligrana (1998), who found annexations in Canada to be based on urbanization and urban development pressure.In Ontario, Meligrana (1998) found that the provincialTable 5.2 Summary of Case Study Municipalities Municipality
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".