“An Occupational Hazard”: Former Elite Male Professional Players’ Experiences of On-Field Violence in Australian Football (1970 to 1995)
Bibliographic record
Abstract
This oral history research explores the experience of ten retired elite Australian football players during their careers in the period from 1970 to 1995. The ex-players were interviewed about their careers by sports journalist, Mike Sheahan, in the long-running Australia Fox Sports Open Mike television series. The particular focus of this historical research is ex-players’ experience of on-field violence. Findings indicated that ex-players were willing to break the Australian football rules and engage in on-field violence either as intimidation or retaliation against opponents. When ex-players did engage in violent intimidatory behavior, they were cool and callous, and anger rarely played a role. Violent retaliation to opposition player transgressions was either immediate or delayed until a future opportunity presented itself. For one Indigenous ex-player, violent responses during games were often sparked by opponents’ verbal racial abuse. In retrospect, he considered this a form of intimidation aimed at putting him off his game that was just part of Australian football at the time. Some ex-players did feel remorse about their violent acts, but others were adamant that they had no regrets about their behavior. Violence was almost expected as an everyday aspect of their football experience and was accepted as an occupational hazard.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".