Un classement multicritère des villes du québec pour favoriser la prise en compte de leurs différences
Bibliographic record
Abstract
ABSTRACT When it comes to making planning and development decisions, the concepts of small, medium‐sized, or large cities are sometimes used to adapt public policies and instruments, or even to highlight challenges that are specific to certain categories of city. In this article, we take a look at the various dimensions that can be used to characterize cities, so as to empirically test a multi‐criteria approach and build a typology of Quebec cities. Using an ascending hierarchical classification, we derive 11 classes of cities, whose conceptualization and graphic representation enable us to highlight their role and, in part, to localize their polarized area. This article complements and adds to works undertaken by other researchers over the last 20 years. Although it does not aim to propose specific changes to public policies and instruments, this article may serve to inform public decision makers in the development of such policies and instruments, and to enrich academic debates on the nature of small and medium‐sized towns in particular .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".