An inch away from being mentally tough: Performance bias in ratings of mental toughness
Bibliographic record
Abstract
Are assessments of an athlete’s mental toughness influenced by how that athlete performs in a single moment in a game? We conducted three experimental studies to address this question and conclude that the answer is yes. In each study, sports fans (total N = 1,097) read vignettes that depicted a mentally tough basketball player, either by describing the player as having many mentally tough attributes (Study 1), or by stating that the player had been identified as being mentally tough by an expert sport psychologist (Studies 2 and 3). Participants then read that the player was about to take a championship-winning shot and were randomly assigned to learn that the shot had been either successful or unsuccessful. Moreover, in Studies 1 and 2 participants learned that the outcome had been either decisive (i.e., a “perfect swish” or an “air ball”) or indecisive (i.e., the ball hitting the backboard, then the rim and, eventually, either going or not going into the basket). In each study, despite learning that the athlete was very mentally tough, participants’ mental toughness ratings depended on whether or not the shot was successful. Ratings were also sensitive to the way in which an outcome was attained: ratings decreased in a linear pattern with the highest ratings after a decisive success, followed by an indecisive success, an indecisive failure, and the lowest ratings after a decisive failure. This research supports the criticism that evaluations of mental toughness are distorted by how an athlete performs in a single moment.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".