Sport officiating recruitment, development, and retention: A call to action
Bibliographic record
Abstract
The purpose of this article is to report on the outcome of a two-day consensus-building exercise amongst sport scientists and sport practitioners interested in the recruitment, development, and retention of sport officials. Twenty participants including volunteers and paid employees affiliated with nine Ontario-based sport organizations, university researchers, and provincial government policy makers participated. A consensus statement regarding this aspect of sport officiating and, more specifically, “What do we know?”, “What don’t we know?”, and “Where does the research need to go from here?” is presented. A willingness to consider and embrace these ideas may be critical in moving sport officiating from being an understudied and undervalued segment of the sport system to receiving the attention and respect it deserves going forward.
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 | 0.493 | 0.410 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.040 | 0.023 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.014 | 0.032 |
| Research integrity | 0.038 | 0.056 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".