“Bringing Cities Back In” To Canadian Political Science: Municipal Public Policy and Immigration
Bibliographic record
Abstract
Abstract Usually, the state of urban research in Canadian political science leads to pessimistic evaluations. This pessimism is belied by one emerging area of study: research on Canadian municipal public policies and immigration, which has flourished over the last 20 years. This article tracks the evolution of this research. First, I retrace how municipal policies for immigrants have been studied, and show how comparison is a central component of this literature. Second, I analyze the dynamics of agenda setting, as well as variables for decision making and implementation. Third, I make three propositions for future research, which are i) to examine the reciprocal relationship between attitudes towards immigration and local public policies and politics; ii) to study local public policy as constructions rather than responses and iii) to revisit the use of national models of integration for cities. In conclusion, I underline the positive outcomes of “bringing cities back in” to Canadian political science, not only to better understand political regulation and Canadian federalism, but also to have a more complete view of the immigrant integration policies.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.022 | 0.030 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".