Sports Officials and Parents as Spectators
Bibliographic record
Abstract
We are very fortunate to be speaking with Associate Professor Tom Webb and Professor Camilla Knight. Dr Tom Webb is an expert in the global management, leadership, operational environment and working practices of sports officials. Dr Webb has published extensively on sports officials, including two books and over 40 peer-reviewed journal articles and book chapters. Tom's research interests concern the role of match officials in sport. Specifically, Tom founded the Referee and Match Official Research Network and has been awarded grants and funding from agencies such as UEFA, the Premier League, World Netball and the European Commission. Dr Camilla Knight has been working at Swansea University for the last few years, after completing her BSc and MSc at Loughborough University, and PhD and postdoctoral position at the University of Alberta in Canada. She is also the lead of the Welsh Research Advisory and Evaluation group for the Child Protection in Sport Unit, a member of the Welsh Safeguarding in Sport Strategy Group, and the Youth Sport lead for the Welsh Institute of Performance Science. Camilla's research interests are concerned with understanding and enhancing the psychosocial experiences of children in sport, with a particular focus on the influence of parents.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.114 | 0.018 |
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".