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Record W2605930244 · doi:10.1017/s0008423915000785

“Bringing Cities Back In” To Canadian Political Science: Municipal Public Policy and Immigration

2015· article· en· W2605930244 on OpenAlexaffabout
Aude-Claire Fourot

Bibliographic record

VenueCanadian Journal of Political Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsImmigrationPessimismPoliticsImmigration policyPolitical sciencePublic policyPublic administrationState (computer science)FederalismPolitical economySociologyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.004
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.336
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2015
Admission routes2
Has abstractyes

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