Bibliographic record
Abstract
This essay examines Leigh Hunt’s attitude towards sport, a subject about which he had mixed feelings. On the one hand Hunt strongly denounced violent and death-dealing sports such as boxing, shooting and angling, and on the other he idealised those pastimes which seemed to him to call back a lost world of ‘Merry Old England’: cricket, quarterstaff and bowls. Hunt saw sports as ethics-in-action; to him they offered moral lessons, some malign (the corrupting effects of pugilism) others benign (the healthy influence of the sports of the village green). The essay also examines how Hunt’s examination of sport was made in gendered terms of ‘manliness’ and ‘effeminacy’, and concludes by arguing that Blackwood’s attack on Hunt and the so-called ‘Cockney’ school might usefully be read in the context of the contemporary satirical tradition of the ‘Cockney sportsman’.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".