The future for sport officiating research: an expert statement
Bibliographic record
Abstract
Research, coverage, and understanding in sport officiating related scholarly activity have increased markedly in the last decade. Sport officials (referees, judges, umpires) have been historically underrepresented in the sport management, psychology, and physiology literature, but this collection of experts provides avenues for collaboration and exploration that can contribute to understanding systems, individuals, and initiate real-world changes for sporting organisations, policy makers, and officials themselves. Focused and organised around the key research areas and priorities of physiology, decision making, psychology, mental health, management, and training and development, this statement offers detail on the development of the research and associated literature and provides proposals for future scholarship linked to each of the key research areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.176 | 0.245 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.038 | 0.048 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".