Determinants of Factors that Influence Small Ruminant Livestock Production Decisions in Northern Ghana: Application of Discrete Regression Model
Bibliographic record
Abstract
The study applied Multinomial Logit (MNL) model to survey data from a sample of 249 farm households in northern Ghana. The model was used to investigate factors that influence farm families ’ decisions to raise a particular small ruminant species (i.e., sheep or goat or both). The MNL analysis indicates that the probability of raising sheep, goat or both animals was influenced by agro-ecological zone, sex and religious background of farmers, risk attitude and income from small ruminant production. In devising strategies (to select farm households) to improve subsistent small ruminant production system, livestock technical staffs must recognize important demographic and farm characteristics as well as risk and income perceptions associated with sheep and goat of the households. In addition, such programs must be supported with improved livestock housing technology that can provide opportunities to control sheep and goat production risks, including theft or predator attacks, disease infestation and exposure to harsh environmental conditions (rains and sun rays).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".