Socio-economic Challenges Deterring Sustainable Pastoralism Among Women Pastoralists in the Sahel Region of Northern Nigeria
Bibliographic record
Abstract
Female pastoralists exhibit great strength in the drive to make a dependable livelihood from livestock tending. This is not without challenges that are gender- specific. This paper examines the socio-economic and environmental challenges faced by women pastoralists in the Sahelian Region of Northern Nigeria. Primary data were derived from an interview of 2,290 adult female household members in 6 local government areas in Bauchi and Gombe States in Nigeria. A stepwise regression analysis determined that amongst 23 socio-economic variables, 14 were significant explanatory or predictive variables (p < 0.05) for the socio-economic status of women, as a measure of their capacity to sustain pastoralism. The results indicate that the length of time or experience in pastoralism had the most predictive power (β = 0.31; p < 0.05), and contributed 13% (R 2 = 0.13) in enhancing the socio- economic status of pastoralist women. This was followed by other socio-economic factors such as the level of formal educational attained, participation in household livestock raising, ownership of large livestock, climate change awareness, prevalence of out-of-school children within the household, availability of household transportation means, category of health care facility accessed, involvement in non- agricultural economic sectors, involvement in food crop farming, ownership of small livestock, amount of rest/sleep affordable, membership of community development groups, and age. The paper recommends support for female education in pastoral communities, access to health care in remote areas, and upgrading community development groups to cooperative or self-help groups that can provide affordable loans to assist pastoralist women in thriving better in a supposedly male-dominated profession.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".