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Record W4221075346 · doi:10.1163/17087384-bja10062

The Imperative of Engendering an Egalitarian Legal Framework for the Protection of Female Employees’ Rights in Nigeria

2022· article· en· W4221075346 on OpenAlexvenueno aff
David Tarh-Akong Eyongndi

Bibliographic record

VenueAfrican Journal of Legal Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsInternational human rights lawConventionLawPolitical scienceLinguistic rightsConvention on the Elimination of All Forms of Discrimination Against WomenDeclarationConstitutionRight to propertyInternational lawCharterRemunerationFundamental rights

Abstract

fetched live from OpenAlex

Abstract This article through doctrinal approach, examines both international and domestic legal frameworks (International Labour Organization Equal Remuneration Convention 1951, Discrimination (Employment and Occupation) Convention 1958, Maternity Protection Convention 1952, Beijing Declaration, Universal Declaration of Human Rights, 1948, African Charter on Human and Peoples Rights, 1988, Protocol to the African Charter on Human and People’s Rights on the Right of Women in Africa, Convention on the Elimination of all forms of Discrimination against Women, 1999 Constitution, Employee Compensation Act, etc that protect female employees rights in Nigeria. It discusses “right” and general employees’ rights in Nigeria howbeit tangentially. It discusses Nigeria’s female employees’ rights and evaluates the laws on general employees’ rights and found that, the vulnerability and peculiarities of female employees, precipitated by their multiple roles in not well recognized and protected under Nigerian labour law when compared with international instrument. It makes vital recommendations on way forward.

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)0.011
metaresearch head score (Gemma)0.010
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 score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.012
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0040.005
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 head0.066
GPT teacher head0.375
Teacher spread0.309 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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