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Record W2996088303 · doi:10.1163/17087384-12340040

Legal Framework to Gender-Based Violence, Sexual and Reproductive Health Rights of Indigenous Women in Cameroon

2019· article· en· W2996088303 on OpenAlexvenueno aff
Patrick Ageh Agejo

Bibliographic record

VenueAfrican Journal of Legal Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousReproductive healthLegislationPsychological interventionGovernment (linguistics)Political scienceReproductive rightsSustainable developmentEconomic growthHuman rightsAffect (linguistics)SocioeconomicsSociologyEnvironmental healthMedicineLawNursingPopulationEcology

Abstract

fetched live from OpenAlex

Abstract Men and women have different health profiles which necessitate different health needs, as a result of their biology and their distinct status in society. Discrimination and harmful traditional practices in many societies in the global south further affect the reproductive health of indigenous women. The paper will highlight discrimination against women in patriarchal indigenous communities in Cameroon. The paper focuses on violations that affect women’s reproductive health. The paper will discuss these violations in light of the country’s commitment to Sustainable Development Goal No. 3 on good health and well-being and Goal No. 5 on gender equality. The paper will also highlight the national and international laws addressing the right to the reproductive health of indigenous women. It will also examine gender-sensitive interventions, legislation and policies put in place by the indigenous community and the Government of Cameroon if any. The paper will end with conclusion and suggestions/recommendations on ways to improve the reproductive health of indigenous women in Cameroon.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.090
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.011
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.000

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 head0.034
GPT teacher head0.332
Teacher spread0.298 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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