Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".