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Record W2886998678 · doi:10.18584/iipj.2018.9.3.2

Women's Use of Indigenous Knowledge for Environmental Security and Sustainable Development in Southwest Nigeria

2018· article· en· W2886998678 on OpenAlexvenueno aff
Yetunde A. Aluko

Bibliographic record

VenueInternational Indigenous Policy Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican cultural and philosophical studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousTraditional knowledgeSustainable developmentFood securitySocioeconomicsAgricultureEconomic growthYorubaQualitative researchSustainable agricultureSocial capitalGeographySociologyPolitical scienceSocial scienceEconomicsEcology

Abstract

fetched live from OpenAlex

Indigenous women are important part of a community’s social capital. This study examined women’s use of Indigenous knowledge (IK) for environmental security and sustainable development in southwest Nigeria. Qualitative data was collected using in-depth interviews conducted among 80 purposively selected rural Yoruba women. The data were analysed using descriptive tools such as frequencies, percentages, and content analysis. The findings reveal an extensive wealth of IK used in agriculture, food processing and preservation, family health care, and child care. The findings also suggest that paying attention to IK will help to incorporate culture as part of rural development and sustainable development in Nigeria, leading to more successful outcomes using place-based knowledge. Indigenous women can, and should, contribute to the design and implementation of sustainable development initiatives because of their extensive IK.

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)0.001
metaresearch head score (Gemma)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.316
Teacher spread0.280 · 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

Citations34
Published2018
Admission routes1
Has abstractyes

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