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Record W245000124 · doi:10.18174/15446

Targeting married women in microfinance programmes: transforming or reinforcing gender inequalities? : evidence from Ethiopia

2010· dissertation· en· W245000124 on OpenAlexfundno aff
H. Bekele

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
FundersInternational Livestock Research InstituteMinistry of Education, IndiaInternational Fine Particle Research InstituteManitoba Health Research Council
KeywordsMicrofinanceEmpowermentScrutinyBargaining powerRemittanceOrder (exchange)Women's empowermentFocus groupEconomic growthBusinessPolitical scienceEconomicsMarketingFinance

Abstract

fetched live from OpenAlex

Existing research on the impact of microfinance on women's empowerment (the intrahousehold gender relation) leads to conflicting conclusions.This may be due to differences in research methodology, scope and focus of the studies (Kabeer, 2001), and the resulting emphasis and interpretation of the research findings.It might for instance be possible that researchers overlook how microfinance intervention improves one dimension of gender relations while undermining another.Secondly, the different programme outcomes in different countries may arise from socio-cultural differences.The prevailing gender-based divisions and customary practices in different countries and communities may influence programme outcomes in different ways.This is because the internal organisation of the Research objectiveThe objective of this study is to investigate if and how microfinance aimed at married women may affect the intra-household division of labour and the decision-making power within households.The study focuses on married women because they live in conjugal relationships with men, which will help us to better understand intra-household power dynamics.Research questions:1. Does women's participation in a microfinance programme affect decision-making patterns within households, and, if so, how? 2. Does women's participation in microfinance affect intra-household labour divisions and responsibilities, and, if so, how?16 Central Statistics Agency

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.280
Teacher spread0.227 · 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.

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

Citations3
Published2010
Admission routes1
Has abstractyes

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