High tackle headache: implications of referee agreement for tackle height law change
Bibliographic record
Abstract
Objectives: Rugby Union has a relatively high risk of injury. Early evidence suggests a benefit of lowering tackle height to reduce head and neck injuries, although concerns persist among stakeholders regarding implementation challenges. This study aimed to understand whether referees can reach the same conclusion regarding tackle height in a controlled environment (ie, video) and whether priming influenced these decisions. Methods: Forty-eight active referees completed a questionnaire based on high-tackle decision-making guidelines after watching tackles. Participants were randomly assigned one of two instructional videos containing a high or legal tackle to investigate the impact of priming on law interpretation. Results: The percent agreement regarding tackle height was 78.1% between participants, 62.7% between participants and an experienced analyst, and 74.0% between participants and a gold-standard referee. Mean intra-rater reliability when determining whether a tackle was high was substantial (percent agreement: 91.2%). For high tackles, 83% of participants agreed on the danger level, 57% on the contact location and 71% on the presence of mitigating factors. No significant effects of priming were observed. Inter-rater agreement among participants and the gold-standard referee was moderate for all items except danger and height, which showed strong agreement. Conclusion: These results suggest a need for improved referee training to support changes to the legal tackle height.
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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 | 0.055 | 0.302 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".