“Even the goats feel the heat:” gender, livestock rearing, rangeland cultivation, and climate change adaptation in Tunisia
Bibliographic record
Abstract
Women's contributions to rangeland cultivation in Tunisia and the effects of climate change upon their livelihoods are both policy blind spots. To make women's contributions to rangeland cultivation visible and to provide policy inputs based on women's needs and priorities into the reforms currently being made in the pastoral code in Tunisia, we conducted fieldwork in three governorates. We conducted focus groups and interviews with 289 individuals. We found that men and women are negatively affected by rangeland degradation and water scarcity, but women are additionally disadvantaged by their inability to own land and access credit and by drought mitigation and rangeland rehabilitation training that only target men. Women are involved in livestock grazing and rearing activities to a greater extent than is assumed in policy circles but in different ways than the men from the same households and communities. Understanding how women use rangelands is a necessary first step to ensuring that they benefit from rangeland management. Women's growing involvement in livestock rearing and agricultural production must be supported with commensurate social and economic policy interventions. Providing all farmers with appropriate support to optimize rangeland use is particularly urgent in the context of resource degradation accelerated by climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".