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Record W4385876939 · doi:10.1080/02660830.2023.2246189

Imagining collective futures in settlement education: Perspectives of Tamil-Canadian immigrant women

2023· article· en· W4385876939 on OpenAlexaffabout
Abarna Selvarajah

Bibliographic record

VenueStudies in the Education of Adults · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSettlement (finance)TamilGender studiesSociologyNeoliberalism (international relations)ImmigrationDiasporaPolitical sciencePolitical economyLaw

Abstract

fetched live from OpenAlex

Literature on adult settlement and integration education in Canada documents the limits of public services supporting the settlement of female newcomers. This study provokes deeper understandings of these limitations by examining the gendered experiences of mature Tamil women who have resided in the province of Ontario, Canada for more than 10 years. Using personal interviews and archival immigration policy documents, this paper argues that despite interacting closely with settlement education programs, mature Tamil immigrant women continue to face gendered and classed barriers to social integration within and outside their communities. Settlement education policies produce temporariness in mature women by making them ineligible for services and supports, thus further scripting their lives on the fringes of their communities. However, mature Tamil immigrant women reject the essentialization of their narratives as continuous historical victims by engaging in relationships with their peers. Friendships between migrant and diaspora women emerge as a unique space to explore agentic resistance to homogenising settlement and integration structures. Theoretical frameworks of this study are anchored in literature discussing neoliberalism, multiculturalism, settlement education, and transnational feminism.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.227
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.045
GPT teacher head0.450
Teacher spread0.405 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

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