Bibliographic record
Abstract
Video games frequently invoke high-pressure circumstances in which player performance is crucial. These high-pressure circumstances are incubators for 'choking' and 'clutching'-phenomena that broadly address critical failures and successes in performance, respectively. The eruption of esports into the mainstream has vitalized the need to understand performance in video games, and particularly in competitive games spaces. In this work, we explore the potential mechanisms behind choking and clutching and how they are related to player traits and tendencies. We report the results of multiple regression analyses, finding that the propensity to choke is positively correlated with Reinvestment, Obsessive Passion, and Public Self-Consciousness, as well as Approach and Avoidance coping styles. Likewise, we find that the propensity to clutch is negatively correlated with Social Anxiety, and positively with Private Self-Consciousness and player experience with competitive gaming. We propose that these findings can be utilized to scaffold and support performance in high-pressure gaming spaces, such as esports. This work represents an initial step in the empirical exploration of choking and clutching in competitive video game contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.012 |
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".