The role of expectations, attributions, and consequences in the appraisal of threat for upcoming competitions: an online and a field study
Bibliographic record
Abstract
If a team loses across multiple games, perceives this as threatening, is unable to cope and team functioning suffers, we call this a team performance crisis (Buenemann et al., under review). Based on further development of crisis decision theory (Sweeny, 2008), it is suggested that high expectations, uncontrollable cause attributions, and high consequences of game outcome may trigger threat states of upcoming competitions in team sport athletes. In an online study, the three factors were manipulated using case vignettes, but no significant effects were found. However, this might be due to the imaginary situation. In a follow-up study, team sport athletes evaluated expectations, consequences, and controllability before and after actual competitions. Threat of the upcoming game was measured using a German translation of the Challenge and Threat in Sports Scale (Rossato et al., 2018), and the difference between demands and resources. For now, N=131 athletes (25 teams) took part before and after competition. Preliminary results reveal an influence of consequences (b=.21; p
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".