Determinants of Participation Decision and Levels of Participation in Small Ruminants Market
Bibliographic record
Abstract
Small ruminants are mainly kept for immediate cash sources and they are also sources of foreign currency. Nonetheless, there is a lack of well-functioning marketing systems. In addition, the different live animals supplied to the market by pastoralists and farmers do not meet the quality attributes required by diverse markets. Randomly 1120 farmers were selected and using double hurdle model, the article identified determinants of participation decision and level of participation in small ruminants market in 7 districts of five regional states of Ethiopia. Out of the total interviewed households, 77.3% and 22.7% were participated and not-participated to the small ruminants market, respectively. The first-hurdle model estimation results for participation decision indicate that Region, access to credit, distance to the market, distance to veterinary service, extension contact and access to market information were found that significantly influenced small ruminants’ market participation The results also show that most of the factors determining decision of participation also determined the level of small ruminants market participation. Therefore, government or any other bodies who are concerned on small ruminants product should help producers on Improving the accessibility of market places; need to facilitate a long term relationship with different actors in order to get reasonable price for the producers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".