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Record W2969287553 · doi:10.1111/cag.12561

Syrian refugee resettlement and the role of local immigration partnerships in Ontario, Canada

2019· article· en· W2969287553 on OpenAlexafffundvenueabout
Margaret Walton‐Roberts, Luisa Veronis, Sarah V. Wayland, Huyen Dam, Blair Cullen

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsMcMaster UniversityUniversity of OttawaWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRefugeeImmigrationEmbeddednessPolitical scienceGovernment (linguistics)Immigration policyOral historyEconomic growthPublic administrationGender studiesSociologySocial scienceLawAnthropology

Abstract

fetched live from OpenAlex

We examine Canada ' s recent Syrian Refugee Resettlement Initiative (SRRI ) paying close attention to the resettlement role played by mid‐sized urban communities. We elaborate on a key policy dimension at work at this scale of action: local immigration partnerships (LIPs). We start with a very brief review of Canada's history of mass refugee resettlement. Second, we assess the policy of LIPs, particularly how they have been presented as a form of “place‐based policy,” and third, we offer an overview of the role the LIPs played in three case study communities (Hamilton, Ottawa, and Waterloo) during the SRRI. Finally, we present three overarching themes that emerged from our research in each of these communities: the importance of each community's history of immigration and refugee resettlement; the embeddedness of the LIP and its leadership in the local community; and how the positioning of each LIP relative to the three levels of government and its official Resettlement Assistance Program agreement holders impacted its ability to act. The history, location, and place characteristics of each community influenced the nature of intersectoral and intergovernmental relations in distinctive ways, and differentially shaped the effectiveness of each LIP's ability to contribute to the SRRI.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.200
Teacher spread0.192 · 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 designObservational
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

Citations27
Published2019
Admission routes4
Has abstractyes

Explore more

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