The refficacy scale: A premilinary investigation to develop a referee efficacy scale
Bibliographic record
Abstract
Sport officiating is a difficult job. One social-cognitive variable that may influence the effective performance of a sports official is his/her self-efficacy beliefs. Based on self-efficacy theory (Bandura, 1977, 1997), highly confident (i.e., high self-efficacy) referees should be more accurate in their decisions, more effective in their performance, more committed to their profession, and have more respect from coaches, administrators, and other officials. In this study, we defined referee efficacy (refficacy) as the extent to which referees believe they have the capacity to perform successfully in their job, and began a preliminary investigation to develop an instrument to measure the concept. A total of 1,988 referees from the U.S. and Spain and from a variety of different sports completed 38 items relating to referee efficacy. Approximately 50% of the sample was randomly chosen to conduct an exploratory factor analysis (EFA). Preliminary results of the EFA indicated that a single factor structure might be most appropriate for the Refficacy Scale (ReffS). All items were retained and the remaining sample was used to conduct a confirmatory factor analysis (CFA). Results from the CFA indicated a moderate model fit (CFI = .835; NNGI = .825; RMSEA = .044) on the one-factor model where all items loaded significantly (p
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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".