INNOVATION IN SUBLOCAL ENTITIES?
Bibliographic record
Abstract
Recently, canadian provinces have undertaken major amalgamation reforms in metropolitan regions. Be it to form a megacity ( in Toronto) or a unicity ( in Winnipeg), the governmental reform aimed at increasing the administrative capacity at the local level while putting in place conditions favorable to the emergence of a collaborative and cohesive approach to urban planning in the metropolitan region. In some case, the structure of the amalgamated city is divided into two levels: the central city level and the sublocal level. Two structural models therefore have been implemented: the unified model with centralized services only, and the two level model with decentralization. The two cases being present in the canadian experiences, the opportunity is given to researchers to compare and analyze the two models. In fact, little attention is given to the sublocal level in studies on amalgamated cities. They are rarely mentioned as features of the new cities although, as administrative and political devices, they represent a challenging question in many respects. Are there different rationales underlying the decision to give a two level structure to the amalgamated cities? Why is this model used in certain cases and not in others? How can such a difference in approach be explained? With the reorganization of municipal institutions in the metropolitan regions, new administrative and
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".