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Record W4410744971 · doi:10.24908/mqup.34569

Social Resilience and International Migration in the Canadian City

2025· book· en· W4410744971 on OpenAlexaboutno aff

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2025
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)Economic geographyPolitical scienceSociologyGeographyDevelopment economicsEconomics

Abstract

fetched live from OpenAlex

This timely volume examines how policies, institutions, and places influence the lives of immigrants and temporary migrants to Canada and how, in turn, those newcomers transform the cities in which they live. Social Resilience and International Migration in the Canadian City draws attention to disparities in outcomes for migrants and proposes strategies to enhance their participation in cities of all sizes. Focused on Ontario and Quebec, chapters pinpoint factors that affect the settlement and integration of immigrants, as well as growing numbers of international students, foreign workers, and refugee claimants. Contributors illustrate how federal, provincial, and municipal policies and diverse institutions – from grassroots churches to settlement agencies – can influence migrants’ capacity to navigate and leverage the resources required to overcome integration challenges. The book’s social resilience framework attends to the social supports that empower migrants to take collective action for their own futures. As migrants interact with a broad range of institutions, those institutions are transformed and become more resilient themselves. Directed at a wide audience of community and government practitioners, migration policy experts, scholars, and civil society activists, Social Resilience and International Migration in the Canadian City provides crucial insight about the policies necessary for helping both migrants and cities thrive, offering ideas for effective implementation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.956
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.218
Teacher spread0.189 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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