A Citation Network Analysis of Perfectionism in Sport
Bibliographic record
Abstract
Perfectionism in sport has received a large amount of attention in both the scholarly and popular domains. Early perfectionism research was conducted in clinical populations and students. Recently this moved towards a focus on athletes and the field has since grown exponentially. Reviews in the form of meta-analyses from Hill and colleagues (2018, 2020) have provided useful insight into the conceptual and theoretical advances in the field, yet there remain gaps to be addressed. Citation network analysis provides a functional method for consolidating literature, specifically examining (1) the lineage of foundational papers to contemporary work, (2) the theoretical and methodological approaches utilized in the field, (3) and the nature of the participants that have been investigated. A three-step scanning process was implemented to identify and screen articles which examined perfectionism in sport. Multiple databases were searched using words such as “sport” and the boolean term “perfection*” to find peer reviewed articles published in English. There was no criteria such as age, gender, sport participated in, or date of publication while searching. The search yielded a total of 158 articles which all varied greatly in the sport consisting of 34 unique sports such as hockey, football, cross country, and track and field. The majority of studies were conducted in the United States, Canada, Australia, and the United Kingdom. Overall, 93% of the articles reviewed implemented a quantitative research method, 4% applied qualitative methods, and 3% were a mix between quantitative and qualitative. The citation network analysis provides detailed insight to identify potential pathways that may yield fruitful findings in the future. Presentation Time: Thursday, 11 a.m.-12 p.m.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".