Bibliographic record
Abstract
The social significance of equestrian competition in antiquity is well documented. Likewise, we know the chronological history of the various events, and a variety of sources—such as the epigraphic record, Pindar, Posidippus, and victory lists—give us a reasonably good idea of who won, when, and where. When it comes to logistics, however, we know very little; this becomes even more apparent when we compare horse sports with other athletic events. In terms of the practicalities of breeding equine athletes in the ancient world, we have relatively little specialized primary material to work with. This article seeks to fill the gaps in our knowledge by taking a comparative approach to the topic. From a physiological standpoint, the horse has changed little since its domestication. The basic nutritional requirements, husbandry methods, and training approaches have remained quite static. This is particularly true in the breeding and training of racehorses, where tradition runs deep, even in the world of Thoroughbred racing. By comparing several aspects of modern racehorse breeding with the evidence from antiquity, we can begin to build a more comprehensive picture of the logistics of equestrian competition, adding not only to our understanding of equines in the ancient world, but also to the field of ancient athletics.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".