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Record W2095927177 · doi:10.5539/ies.v1n2p115

Gender Disparities in Secondary Education in Bangladesh

2008· article· en· W2095927177 on OpenAlexvenueno aff
Molla Huq, Pk. Md. Motiur Rahman

Bibliographic record

VenueInternational Education Studies · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
FundersUniversity of Dhaka
KeywordsRetention rateAcknowledgementDropout (neural networks)DemographyStipendHigher educationDemographic economicsPsychologyEconomicsEconomic growthPolitical scienceSociologyBusiness

Abstract

fetched live from OpenAlex

Enrolment and success rates are very crucial for any educational system in the world but they are more important for the developing countries like Bangladesh. Gender differences in enrolment and success rates are also emerging issues. This study investigated the enrolment and success rate’s status in secondary educational system of Bangladesh along with internal efficiency rates involving completion rate, retention or survival rate, dropout rate and gender parity index. Some tests of hypotheses of homogeneity were also conducted. Among the findings higher female enrolment rate, lower female success rate, higher male completion rate, survival rate and higher female dropout rate were perceived along with different location, types of schools variations. Some policy implications were suggested with acknowledgement of some flaws in the female stipend Program in Bangladesh.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.048
GPT teacher head0.371
Teacher spread0.323 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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