Researching one’s own refereeing sports study program: Benefits of an approach based on involvement from the “right” distance
Bibliographic record
Abstract
As a physical education teacher and a professional referee for the French Football Federation, the author has led the football refereeing program in Auxerre for 14 years. The program offers students the opportunity to specialize in refereeing. In the context of the author’s doctoral research, his duties and responsibilities within the program provide him with greater insight into how a refereeing school operates, notably through his position as an involved researcher, which merits further explanation. His position provides a number of benefits. However, it also offers apparent benefits in terms of access to “proximate fields,” which can turn out to be difficulties. Therefore, the nature of his position requires a process of critical reflection on the right perspective. In other words, there are means and resources available to researcher-actors that can help to limit the impact of the biases generated by having a close relationship to the object of 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 | 0.106 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".