Building communities with new rural regions in Manitoba
Bibliographic record
Abstract
Following several decades of losing population in rural Manitoba, more than 80 communities fell below the minimum requirement of 1000 people needed to maintain their municipality status. In 2012, the Provincial government responded with an amalgamation initiative of modernizing rural municipalities. This initiative allowed municipal jurisdictions to determine their amalgamation partners, a more suitable and collaborative intervention than predetermined amalgamation. In addition to locally driven partner identification, this amalgamation initiative was also notable for being one of the first instances where a national analysis of a functional economic region model at the provincial scale informed the amalgamation process. Provincial and municipal jurisdictions involved in the initiative were provided with a breakdown of the functional economic regions based on where residents live and work. In addition to geographic boundaries, the analysis also provided findings regarding potential population and fiscal strengths for these regions. This presentation provides important insight into municipal amalgamation by examining the overlap between the proposed functional regions and the 47 amalgamations resulting from locally driven 'municipal partnering'. Since the new legislation gives municipalities until 2019 to complete this process, the jury is still out if this initiative will result in strengthening rural regions in Manitoba.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".