An Analysis of Literature on Sport Officiating Research
Bibliographic record
Abstract
Sport officials are crucial members of sport. Researchers have studied their roles numerous times, with results often informing sport procedures (e.g., athlete order in artistic sports). As the research on sport officiating spans five decades and several topics of interest, it is important that researchers periodically synthesize the literature. Purpose: The purpose of this study, therefore, was to conduct an analysis of literature on sport officiating research. Method: Guided by previous researchers, we executed four methodological steps including the article search, article retrieval, sample validity, and article coding. These steps yielded 386 articles for analysis, which ranged from 1971 to 2018. We coded the articles based on four main categories: article information, participant demographics, contextual information, and methodology. Results: Key findings from this analysis include a recent influx in sport officiating research, a vast number of publication journals, few studies dedicated to female-only participants, many studies missing relevant demographic information, an over-representation of interactors, and a reliance on quantitative studies. Conclusions: Though many researchers have conducted studies on sport officiating, several articles had poor methodological rigor (e.g., not reporting key demographic information). In the discussion and conclusion sections, we highlight strengths and weaknesses within the field and provide recommendations to guide future researchers and practitioners, to ensure robust research designs and guide applied practice.
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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 | 0.031 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.110 | 0.077 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".