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Record W2743894230 · doi:10.1163/17087384-12340005

Land Grab, Property Rights and Gender Equality in Pluralistic Legal Orders: A Nigerian Perspective

2016· article· en· W2743894230 on OpenAlexaffvenue
Chilenye Nwapi

Bibliographic record

VenueAfrican Journal of Legal Studies · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsCanadian Natural Resources
Fundersnot available
KeywordsLegal pluralismLand lawStatuteLawPolitical scienceLand tenureLaw and economicsSociologyComparative lawLegal realismGeography

Abstract

fetched live from OpenAlex

This article considers the impact of land grab on the promotion of gender inequality within the Nigerian pluralistic legal order. It examines the interface between customary law and statute law in the determination of land ownership and access in Nigeria. It makes two key arguments. (1) While legal pluralism presents opportunities for curtailing the excesses of customary law, it has often resulted in the dominant legal system – statute law – fostering gender inequality in a manner that is beyond the capacity of the so-called barbaric customary laws. (2) The capacity of law to effectively address the problem of gender inequality within the context of land grab is very limited, because the nature of most land grab-related activities that promote gender inequality are appropriately legal and it is their unintended consequences that undermine women’s rights. The article argues for an effective use of the political process to complement legal interventions.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.034
Scholarly communication0.0070.007
Open science0.0000.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.273
Teacher spread0.224 · 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

Citations13
Published2016
Admission routes2
Has abstractyes

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