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Record W1991364342 · doi:10.5509/2013863471

Empowering Women through Recognition of Rights to Land: Mechanisms to Strengthen Women's Rights in Vanuatu

2013· article· en· W1991364342 on OpenAlexvenueno aff
Vijaya Nagarajan, Therese MacDermott

Bibliographic record

VenuePacific Affairs · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
Fundersnot available
KeywordsLand rightsPolitical scienceLand lawGender studiesSocioeconomicsSociologyLand tenureGeographyEnvironmental planning

Abstract

fetched live from OpenAlex

Although the pluralist system of land tenure in Vanuatu does not directly discriminate against women, the operation of the system and contemporary interpretations of custom are increasingly marginalizing women from decision-making processes regarding land management and control. Commitment to the principles of gender equality through constitutional guarantees and the ratification of relevant international treaty obligations, while providing an appropriate legal framework for equality, have only had limited success in addressing discriminatory practices. This article analyzes alternative ways to overcome the barriers faced by women that are currently under consideration in many Pacific Island countries, including recording and registration, as well as legal vehicles such as incorporating customary land groups, trusts and community companies. This article concludes that while both existing and proposed mechanisms have the potential to secure for women a greater role in decision-making processes regarding land management and control, that potential will not be realized in the absence of knowledge, empowerment and the acceptance of the legitimacy of such rights.

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)0.004
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0040.002
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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