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Record W2485187531

Women, Gender, and Immigrant Studies: State of the Art in Adult Education in Canada

2015· article· en· W2485187531 on OpenAlexaffvenueabout
Hongxia Shan

Bibliographic record

VenueCanadian Journal for the Study of Adult Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTransformative learningImmigrationGender studiesSociologyScholarshipAdult educationEmpowermentFeminismEthnographyPedagogyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Since the 1980s, immigrant studies have taken a feminist turn. This paper takes stock of how Canadian adult educators have contributed to this movement and vice versa. Specifically, it engages in an ethnographic content analysis of CASAE proceedings since 2000, and other scholarly publications in the field. The review shows that increasing research attention has been paid to immigrant women’s experiences. It also reveals competing discourses on the roles that immigrant training programs and community-based organizations have played in the work and life experiences of immigrant women. Theoretically, the literature indicates a resurgent feminist influence. Adult learning theories, such as transformative learning, communities of practice and informal learning, as well as the adult education orientations for liberation, empowerment and social action also feature prominently in the scholarship. While research interest in immigrant women is robust, the existing literature is largely limited by methodological nationalism. It also needs to move beyond a focus on women’s experiences to address gender relations as implicating both men and women, and both individuals and institutions.

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)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.125
Threshold uncertainty score0.910

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.017
Science and technology studies0.0220.018
Scholarly communication0.0150.004
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.028
GPT teacher head0.300
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal for the Study of Adult EducationSame topicTourism, Volunteerism, and DevelopmentFrench-language works237,207