Bibliographic record
Abstract
Circus is an art form that developed around horses and trick riding. Philip Astley, an excavalry man who had recently returned to London after fighting in Europe in the Seven Years War (1756- 63), founded Modern Circus when he introduced clowns, musicians and acrobats to cover the changeover in his riding displays. Daring, acrobatic stunt riding remained the central most important element in modern circus. The strong sense of connection developed between a cavalryman and his horse through the sense of shared mortality on the battlefield was an important element informing the presentation of horses in modern circus. Running counter to the widespread exploitation of horses as beasts of burden widely used as machines, modern circus often depicted horses as creatures of passion, linked to Romantic imagery of the sublime. Astley championed a more humane way of training horses, and, in the context of its time, Astley’s circus can be seen as acting as a social force to contest pervasive cultural attitudes towards horses as machines. New Circus, which began in the late 1970s, saw a move away from the use of animal performers to the use of human performers only. This can be seen as a response to growing concern about the role of animal performers within circus, and also growing awareness of the rights of animals. The emergence of three new horse circuses in Quebec, Canada, namely Luna Caballera, Cavalia, and Saka, all formed after 1999, is examined in light of this cultural context and the work of Cavalia is discussed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".