Bibliographic record
Abstract
Tables 1.1 City council seats obtained in the 2017 Quebec City election 18 1.2City council seats obtained in the 2017 Montreal election 19 1.3 Socio-demographic characteristics and voter turnout 23 1.4 Socio-demographic characteristics and vote choice 26 2.1 Voter sex and vote choice in Montreal 46 2.2 Voter sex and vote choice in Quebec City 46 2.3 Binary logistic regression results (voting for a woman in Montreal -Plante vs Coderre) 47 2.4 Binary logistic regression results (voting for a woman in Quebec City -Guérette vs other) 48 A2.1 Binary logistic regression results (vote choice in Quebec City -Gosselin vs other) 51 A2.2 Binary logistic regression results (vote choice in Quebec City -Laubeaume vs other) 51 3.1 Reported turnout by language group by office, Montreal 65 3.2 Mean voluntary group membership by language, Montreal 66 A3.1 Predicting perceptions of linguistic diversity 74 4.1 Descriptive statistics for Montreal and Quebec City 81 4.2 Municipal candidates and political parties 84 4.3 Logistic regression models: Coderre = 1; Plante = 0 85 A4.1 Multinominal logit regression models: reference = Labeaume 91 5.1 Montreal: Descriptive analysis 102 5.2 Quebec City: Descriptive analysis 103 5.3 Logistic regression Montreal vote 105 5.4 Results for Quebec City 107 6.1 Creation date of the main municipal parties competing in Montreal and Quebec City in the 2017 elections 120 6.2 Distribution of partisanship across levels of government 121
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.868 | 0.635 |
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".