Facilitators and barriers for female sport officials in male-dominated sport
Bibliographic record
Abstract
Sport officials are tasked with making quick and accurate decisions, maintaining order, communicating with athletes and coaches, and enhancing athletes’ safety. In most sports, more male than female sport officials are recruited and retained. The limited research focusing on female sport officials suggests that their experiences are frequently negative. Further understanding female sport officials’ experiences is imperative for learning more about their intentions to begin and continue (rather than quit) as officials. The purpose of this study was to explore the positive and negative experiences of female sport officials who operated in sports where the officials were primarily male. Nine sport officials participated in semi-structured interviews. Thematic analysis was used to identify and code common themes within the data, many of which aligned with the principles of Self-Determination Theory. The main themes discussed herein are (a) The Female Experience (pertaining to the context and environment in which they operated), (b) Facilitators (influences that assist with the responsibility of officiating), and (c) Barriers (circumstances or regulations that have had negative impacts on advancement and development). These themes highlight the inequality females are confronted with in the sport officiating environment, but they also provide helpful tools to promote a more positive environment. By using these tools, female sport officials are more likely to continue and thrive as officials, rather than resign. Recommendations will be provided for sport governing bodies, officiating organizations, and sport officials, which might contribute to future policy changes that lead to increased recruitment and retention of female sport officials.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".