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Record W4386216797 · doi:10.18733/cpi29823

Rohingya Refugee Women Negotiate English and (Ambivalently) Their Children's Schooling

2023· article· en· W4386216797 on OpenAlexfundvenueaboutno aff
Sandra R. Schecter

Bibliographic record

VenueCultural and Pedagogical Inquiry · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRefugeeNegotiationGender studiesSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

An action research partnership focused on social integration of Rohingya refugee women with their children enrolled in Ontario public schools, aiming to better equip caregivers to advocate for their children’s academic interests by creating a social space where Rohingya women could learn English while addressing their children’s schooling needs. The self-constituted group met weekly throughout a year. The study’s method involved a recursive process of gathering and analyzing data; sharing findings with participants; and developing, delivering, and revisiting participation formats and activities. Key findings were: although the project fostered community building and self-empowerment in several areas, unfamiliarity with the Ontario school system and perceived lack of English proficiency prevented the women from developing self-confidence to advocate for their children’s educational needs. Also, while they voiced a commitment to linguistic maintenance and cultural continuity, they found these goals daunting given their children’s English skills already exceeded those in their first language.

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)0.002
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 score0.170
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0100.009
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.367
GPT teacher head0.436
Teacher spread0.069 · 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

Citations0
Published2023
Admission routes3
Has abstractyes

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