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Record W2090894622 · doi:10.1080/026037042000293407

Through a gender lens: explaining North‐Eastern Thai women’s participation in adult literacy education

2004· article· en· W2090894622 on OpenAlexaff
Pierre Walter

Bibliographic record

VenueInternational Journal of Lifelong Education · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLiteracyAdult literacyAdult educationGender studiesSociologyLens (geology)PsychologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

This ethnographic study employs a gender perspective to understand the motivations of eight women literacy learners participating in a village‐based functional literacy programme in rural North‐eastern Thailand. Field research took place over six months of periodic residence in a North‐eastern Thai village, and involved participant observation, individual interviews and informal focus groups. An analysis of the women’s reproductive, productive and community roles, and their practical and strategic gender needs (Moser ) is used to frame findings on the women’s participation in the literacy programme. In brief, although the women valued educational programmes that reduced the burden of their reproductive labour, offered income‐generating opportunities in their productive roles and supported their leadership roles in the community, the village literacy programme had limited effect in addressing these practical gender needs. However, because a patriarchal ideology and Buddhist institutions had denied the women access to schooling as children, they now saw the literacy programme as both a symbolic return to school and a collective women’s space to advocate for more desirable adult educational programmes. In these ways, the programme addressed their strategic gender needs. The study argues that a gender lens is critical in explaining women’s participation in literacy programmes and in designing literacy education for development.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.031
GPT teacher head0.392
Teacher spread0.361 · 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 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

Citations7
Published2004
Admission routes1
Has abstractyes

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