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Record W4385420451 · doi:10.18280/ijsdp.180734

Enhancing Women’s Participation in Community Development Through Community Education for Sustainable Development in South-East Nigeria

2023· article· en· W4385420451 on OpenAlexvenueno aff
Kingsley Asogu Ogbonnaya, Okechukwu Ann E.

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentCommunity developmentEconomic growthEnvironmental planningCommunity participationPolitical scienceGeographySocioeconomicsSociologyEconomics

Abstract

fetched live from OpenAlex

The study focused on enhancing women's participation in community development through community education for sustainable development.Three research questions and three null hypotheses guided the study.Descriptive survey design was used for the study.The population for the study was 828 respondents from south-East states of Nigeria.Researchers' made questionnaire was the major instrument for the study.The research questions were analyzed using mean scores and standard deviation while the null hypotheses were tested using t-test statistics.Based on the analyses, the following major findings was established: (i) encouraging more rural dwellers to participate in agricultural production to enhance their income, providing employment opportunities by creating jobs for rural dwellers among others were how community education enhances socio-economic potentials of women, (ii) enhancing literacy education for rural women, improving capacity building among women among others were the community education that enhanced literacy capacity of women, (iii) inadequate provision of funds by the government for the enhancement of community education and inadequate support from donor agencies for community education among others were the constraints militating against community education in enhancing women's participation in community development.Based on the findings of the study, recommendations were made.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.036
GPT teacher head0.352
Teacher spread0.316 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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