Examining Correlates of Threat States Through the Lens of Team Performance Crises and the Role of Losing Streaks
Bibliographic record
Abstract
This multistudy report tests threat states as central to team performance crises. In a preregistered online study (Study 1), N = 396 athletes answered a questionnaire after reading a vignette to manipulate expectations, attribution, and consequences. In a preregistered field study (Study 2), those factors were tested on N = 161 athletes on competition days. In both studies, expectations, attribution, and consequences were unrelated to threat, but participants with uncontrollable vignettes rated their upcoming match to be less controllable, t(375) = 1.98, p < .05. Study 1 replicated the findings of appraisal literature, linking challenge and threat to emotions, collective efficacy, and task-related cohesion. Study 2 shows the losing streaks of the three are associated with higher threat states, (β = 0.31, p < .05); but two-game losing streaks are not, β = 0.10, p = .277. The studies are discussed in the light of existing literature on crises and threat states.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".