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Record W4407127885 · doi:10.1080/03066150.2025.2451792

Gender, land grabbing and agrarian livelihoods: contradictory gendered outcomes of land transactions in Ghana

2025· article· en· W4407127885 on OpenAlexafffund
Nathan Andrews, John Hopeson Anku, Helen Akolgo‐Azupogo

Bibliographic record

VenueThe Journal of Peasant Studies · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLand grabbingLivelihoodAgrarian societyLand rightsGender relationsAgricultural landBusinessNatural resource economicsEconomic growthGeographyPolitical scienceDevelopment economicsEconomicsAgricultureSociologyGender studiesEnvironmental planning

Abstract

fetched live from OpenAlex

Based on a multi-method research design that employs a triangulation of fieldwork data, including focus group discussions (n = 35) and key informant interviews (n = 5), this paper examines the intervening role of gender in the differentiated distribution of harms and benefits from large-scale land acquisitions (LSLAs). Employing Feminist Political Ecology (FPE) as the analytical framework, this study examines how varying positionalities such as marital status, tenure systems, and local power dynamics shape women’s encounters with LSLAs. Our findings reveal that while patriarchal structures continue to restrict women’s control over land resources, the economic opportunities arising from projects in the Central and Volta regions of Ghana have enhanced some women’s financial independence and decision-making power within their households. This shift, however, has created tensions, as men perceive these changes as a challenge to traditional gender roles. By interrogating these complex gendered experiences, this study enriches the FPE framework, highlighting how intersecting positionalities influence resource access and control in agrarian communities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.133

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.259
Teacher spread0.215 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2025
Admission routes2
Has abstractyes

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