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Record W4381511411 · doi:10.1080/09614524.2023.2220989

Achieving gender equality through challenging social norms: BRAC’s Polli Shomaj program

2023· article· en· W4381511411 on OpenAlexaff
Nayma Qayum, Mirza Hassan, Syeda Salina Aziz

Bibliographic record

VenueDevelopment in Practice · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsTransformative learningCivil societyGender equalityPublic relationsPower (physics)Service delivery frameworkEconomic growthInequalityService (business)SociologyFocus groupPolitical sciencePublic administrationPoliticsEconomicsGender studiesBusinessLawMarketing

Abstract

fetched live from OpenAlex

Can NGOs implement rights-based gender equality programs when donor focus on the area is shrinking? This paper explores how one development program has made strategic choices incorporating the interests of multiple stakeholders, addressing donor interests while simultaneously addressing the needs of local communities. It examines the evolution of BRAC’s Polli Shomaj, a rural women’s civil society organisation designed to challenge power structures through collective action in rural Bangladesh. It draws on interviews with program staff and existing program literature to find that over time, BRAC leadership has narrowed its program focus to shed its broad transformative agenda to focus solely on gender equality through a combination of service delivery and rights-based approaches. The paper suggests that while it is possible for NGOs to promote gender equality through a combination of rights-based and service-delivery approaches, greater focus is needed on challenging power structures to bring about lasting structural change.

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)0.010
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.007
Scholarly communication0.0050.003
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.146
GPT teacher head0.344
Teacher spread0.198 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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