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Record W2971035072 · doi:10.1111/cag.12558

The role of local immigration partnerships in Syrian refugee resettlement in Waterloo Region, Ontario

2019· article· en· W2971035072 on OpenAlexafffundvenueabout
Blair Cullen, Margaret Walton‐Roberts

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRefugeeImmigrationSyrian refugeesPolitical scienceScope (computer science)Economic growthLaw

Abstract

fetched live from OpenAlex

As of January 29, 2017 Canada had received 40,081 Syrian refugees. The scale and scope of this resettlement is historic, with the only comparable event being the arrival of 60,000 Indo‐Chinese refugees in the late 1970s. Since that time, much has changed in local resettlement policy. This research focuses on one component of these changes—the role of Local Immigration Partnerships (LIPs) in Syrian refugee resettlement—through a case study of an official refugee reception centre in the Waterloo Region of Ontario and a series of interviews with key informants from multiple sectors involved in resettlement. Results indicate Waterloo's LIP playing a sizable role, but not acting as the sole response body to refugee resettlement. Nevertheless, participants saw the LIP as a crucial part of Waterloo's resettlement efforts. Despite being a product of a tri‐level intergovernmental agreement, the LIP played a central role in shaping a local strategy by using local solutions. LIPs represent an example of place‐based policy that worked well during the Syrian Refugee Resettlement Initiative, but LIPs’ success may set a challenging precedent for future mass refugee resettlement events.

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 score0.570
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
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 head0.011
GPT teacher head0.215
Teacher spread0.204 · 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

Citations12
Published2019
Admission routes4
Has abstractyes

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