Cattle logic and capital logic: the recalcitrance of transhumance in the establishment of private grazing lands in North West Nigeria
Bibliographic record
Abstract
Abstract Livelihoods associated with transhumance cattle production are increasingly decimated and threatened by serious loss of human lives due to increasing competition for resources as driven by cattle logic and capital logic, thus the plan and drive for livestock transformation have been subjected to the recalcitrance of transhumance in Nigeria. This study examined cattle farmers’ willingness and attitude towards the establishment of private grazing lands in Sokoto State Nigeria. A multi-stage sampling technique was used to select 457 cattle farmers from 10 Local Government Areas of Sokoto State, from which data were collected through a structured questionnaire and subjected to frequency counts, percentages, mean and standard deviation, Probit regression, and Principal component analysis. The results revealed that cattle farmers have unfavorable attitudes and are unwilling to establish private grazing lands. The determinants of cattle farmers’ willingness and attitude to the establishment of grazing lands overlap and include age (t = 1.97; p < 0.05); marital status (t = -11.35; p < 0.05); educational level (t = -2.73; p < 0.05); credit amount (t = -44.56; p < 0.05); source of credit (t = -5.01; p < 0.05); herd composition (t = -2.20; p < 0.05); attitude (t = 8.82; p < 0.05) and constraints (t = 1.97; p < 0.05). The Principal Component Analysis extracted factors are Factor 1 (Resource utilization), Factor 2 (Environment concerns), Factor 3 (practice suitability), and Factor 4 (cattle productivity) and accounted for 21.59%, 6.93%, 6.20%, 5.35% of the variance respectively; with a cumulative 40.06% variance. These results affirm Bartlett’s Test of Sphericity with a value of X2 = 1991.43, p = 0.00, and Kaiser-Meyer-Olkin Measure of Sampling Adequacy of 0.835. It is recommended that a clear distinction of logic for curtailing transhumance which is responsible for farmer-herder conflicts be established and limitations of cattle movements defined within their immediate surroundings.
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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 | 0.002 |
| 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".