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Record W3164104450 · doi:10.5539/jsd.v14n4p13

Passive, Victims or Unintended Misrepresentation?

2021· article· en· W3164104450 on OpenAlexvenueno aff
Fidel C. T. Budy

Bibliographic record

VenueJournal of Sustainable Development · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
FundersAberystwyth University
KeywordsAgency (philosophy)MisrepresentationLand grabbingUnintended consequencesDepictionEconomic growthWork (physics)SociologyPolitical scienceGender studiesGeographyEconomicsLawSocial scienceAgriculture

Abstract

fetched live from OpenAlex

Sustainable development efforts to mitigate the challenges that women face in the midst of land grabbing could be significantly undermined or they could fail to address the concerns of rural African women if they are not driven by the everyday lived experiences of rural African women. Evidence suggests that current accounts of how rural African women experience land grabbing oversimplify the homogeneity of their experiences, depicting them as entirely passive and victims who lack the agency to react to the loss of their land. Addressing this gap in our appreciation of the impact of land grabbing on rural African women is significant to ensure equal access to land and secure tenure rights for women actually work. To this end, there are some in the literature that have, and continue to challenge the depiction of rural African women as entirely passive and victims, lacking agency. This paper builds on those studies to expand the parameter of inquiry by bringing fresh perspectives to the debate from Senjeh District in Liberia. Utilising data collected through qualitative semi-structured interviews in the district over a period of four months, this paper argues that there is a divergence between the well held notions by the literature and experts on the one hand and, women in Senjeh on the other hand. The paper also argues that rural women in Senjeh District exhibited various agency in multiple ways against the loss of their land to Sime Darby.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.229
Teacher spread0.214 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

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