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Record W2969563290 · doi:10.1093/jrs/fez065

Settlement and Integration Policies in Federal Contexts: The Case of Refugees in Canada and Brazil

2019· article· en· W2969563290 on OpenAlexaboutno aff
Catarina Ianni Segatto

Bibliographic record

VenueJournal of Refugee Studies · 2019
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsSettlement (finance)RefugeeCorporate governanceGovernment (linguistics)Public administrationPolitical scienceFace (sociological concept)Economic growthSociologyBusinessEconomicsFinanceSocial scienceLaw

Abstract

fetched live from OpenAlex

Abstract The scholarly literature highlights that refugees face barriers accessing public services due to cultural, language and communication differences, financial constraints and lack of information, which can be reinforced by variations in service coverage and provision, especially in low- and middle-income countries, and complex intergovernmental relations and governance arrangements, common in federal systems. This article seeks to analyse Brazil and Canada to understand the implications of intergovernmental relations, particularly the role of local governments in settlement and integration policies. The analysis of documents and in-depth interviews highlights that, although Canada is highly decentralized, the federal government has a stronger role in guaranteeing equal access to services than in Brazil. Nevertheless, the lack of coordination among governments in both countries reinforces certain barriers, which have been overcome by an increasing role of municipalities and local actors in this policy field.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.622

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0280.011
Scholarly communication0.0060.001
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.358
Teacher spread0.340 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2019
Admission routes1
Has abstractyes

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