Selective breeding of Arabian and Thoroughbred racehorses in Algeria: perceptions, objectives and practices of owners-breeders
Bibliographic record
Abstract
This survey, conducted with 461 racehorse owners-breeders in Algeria between 2009 and 2011, investigates their perceptions, objectives and practices regarding selective breeding. Racehorse breeding is a full-time professional activity for a third of interviewees. The holdings are small-sized with 77% owning one or two mares. The regular practice of mating is here used to categorize breeders according to their degree of professionalization (38.4% professional vs. 61.6% occasional breeders). Experience in the sector was also used to classify breeders, considering as "junior" the breeders under 10 years experience (38.8%) and as "senior" those above 10 years (61.2%). More than professionalization, experience shows a significant impact on practices and objectives. Thus, experience influences breed choice (junior breeders tend to specialize while senior own both Arabian and Thoroughbreds), age at first foaling (sooner among senior breeders), information sources considered for selecting stallions (senior use more diversified sources), the importance granted to the price of mating (greater for junior breeders), the importance granted to the ranking compared to earnings (the ranking being more important to junior breeders), and the priority given to breeding (junior breeders give higher priority to a buy-race-resell activity). Finally, racehorse breeding is poorly professionalized, the only financial goal being cost coverage. Despite inappropriate practices, an interest for selection is noticed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".