Don't Fence Me In: Managing Psychological Well Being for Elite Performance Horses
Bibliographic record
Abstract
This article posits that stereotypical behavior patterns and the overall psychological well being of today's performance horse could be substantially enhanced with care that acknowledges the relationship between domesticated horses and their forerunners. Feral horses typically roam in stable, social groups over large grazing territories, spending 16-20 hr per day foraging on mid- to poor-quality roughage. In contrast, today's elite show horses live in relatively small stalls, eat a limited-but rich-diet at specific feedings, and typically live in social isolation. Although the horse has been domesticated for more than 6000 years, there has been no selection for an equid who no longer requires an outlet for these natural behaviors. Using equine stereotypies as a welfare indicator, this researcher proposes that the psychological well being of today's performance horse is compromised. Furthermore, the article illustrates how minimal management changes can enhance horses' well being while still remaining compatible with the requirements of the sport-horse industry. The article discusses conclusions in terms of Fraser, Weary, Pajor, and Milligan's "integrative welfare model" (1997).
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 | 0.002 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".