MétaCan
Menu
Back to cohort
Record W2994066949 · doi:10.25071/1920-7336.39620

(En)Gendering Vulnerability: Immigrant Service Providers’ Perceptions of Needs, Policies, and Practices Related to Gender and Women Refugee Claimants in Atlantic Canada

2014· article· en· W2994066949 on OpenAlexaffvenueabout
Evangelia Tastsoglou, Catherine Baillie Abidi, Susan M. Brigham, Elizabeth A. Lange

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2014
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMount Saint Vincent UniversitySt. Francis Xavier UniversitySaint Mary's University
Fundersnot available
KeywordsRefugeeService providerImmigrationPolitical scienceVulnerability (computing)Service (business)BusinessLawComputer security

Abstract

fetched live from OpenAlex

As part of a multi-phased study exploring the experiences of refugee claimants in Atlantic Canada, this article focuses on the experiences and perceptions of immigrant service providers in relation to gender and women refugee claimants. Given the paucity of research on refugees in Atlantic Canada and on the particular perspectives of service providers, we have located this part of our research in the intersection of state policies and civil society practices, in particular service providers’ and NGO practices vis-à-vis refugees and refugee claimants. To contextualize our study we briefly trace global and national trends in migration and refugee issues, specifically increasing refugee deterrence policies that restrict claimants’ access to protection and settlement services. Findings highlight the recognition of gender-specific needs but also the lack of a gendered analysis of women refugee claimants, uneven accessibility to support services across the Atlantic region, challenges in navigating services, low cultural competence of institutional social and health service providers, and the rise of a punitive deterrence culture.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.310
Teacher spread0.292 · 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 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

Citations33
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueRefuge Canada s Journal on RefugeSame topicMigration, Health and TraumaFrench-language works237,207