Bibliographic record
Abstract
Interest in perfectionism in sport psychology has steadily increased over the last twenty-five years. The last 10 years in particular has seen a dramatic increase in research dedicated to the topic. As a result, we have learned a great deal about perfectionism in this domain. However, it is also an area of work in which there has been considerable disagreement on key issues; most notably, the degree to which perfectionism is helpful or a hindrance to athletes. A number of new concepts have recently emerged that may help navigate some of the issues that have historically hampered the study of perfectionism: combined and total unique effects, perfectionistic tipping points, and perfectionistic climate. In this short overview some of the latest advances in this area are introduced, explained, and discussed. Each concept offers interesting opportunities for advancing the study of perfectionism in sport. They also each provide avenues for novel research, as well as impetus to revisit previous research and existing data to yield new insights. Most importantly, the concepts offer the promise of taking us closer to our aim of understanding the effects of perfectionism in sport, and better identifying and supporting athletes at risk to its negative effects. • The impact of multidimensional perfectionism on athletes remains controversial. • New conceptual approaches are needed and offered. • Total unique effects reveal the overall impact of perfectionism. • Perfectionistic tipping points show how dimensions act on each other. • Perfectionistic climate resituates perfectionism in the social context with its own effects.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".