Bibliographic record
Abstract
Shared representations and coordinated action, in both team sports and for individuals with specific roles, have a big impact on performance outcomes in a wide range of sporting domains. Within team sports, perceptual and decision-making issues are key; performers must both see things in similar ways and make similar or mutually compatible decisions if appropriate action is to be taken and performance optimised. To do this, an athlete must interpret perceptual information effectively, applying an implicit ‘weighting scale’ to determine the pertinence of key factors. Such a commonality of perception across a team allows the formation of a shared mental model (SMM) through a process of both time-pressured and deliberate thinking and action, along with appropriate feedback. As individuals, referees and match officials must apply a consistent weighting scale to both formal decision making (i.e. applying the rules of the game) and more informal game management. The implementation of SMMs has been shown to increase consistency and coherence in leading referees, with shared representations resulting from both training and more ‘natural’ processes. Selection panels have also demonstrated substantial and rapid improvements in coherence, through exposing and agreeing operational definitions of key criteria, developing common weighting scales, monitoring and regular feedback. Such shared representations also carry benefits for support personnel within interdisciplinary teams and for team cultures; common features in high performance sport. It is clear that socially based or developed shared representations are crucial to effective performance in sport, and the various examples considered here offer considerable potential for future research.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| 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".