Exploring athletes’ and classifiers’ experiences with and understanding of classification in Para sport
Bibliographic record
Abstract
Classification is a defining feature of Para sport; however, little empirical evidence describes the experience of classification and how it can be improved. To date, the primary focus of research related to classification has been on the development of evidence-based classification procedures. Meanwhile, the limited literature which has focused on experiential aspects of classification has shown classification to be a potentially negative experience for athletes. As well, classifiers have been identified as important social actors within the Para sport context, yet no research has examined both athletes’ and classifiers’ experiences with classification. The experiences of athletes and classifiers have yet to be considered alongside one another. Therefore, the purpose of this study was to elucidate athletes’ and classifiers’ experiences with classification in Para sport. Semi-structured interviews exploring the experience of classification were conducted with 18 internationally classified Canadian athletes and an international sample of eight classifiers. Hermeneutic phenomenological analysis was used to conceptualise athletes’ and classifiers’ classification experience. Results demonstrate athletes and classifiers learn about classification by observing others and reflecting on their own understanding of their body or skillset in relation to classification. Additionally, we show how interactions between athletes and classifiers influence each parties’ experience quality and highlight discrepancies between each groups’ understandings of classification. Next, we provide recommendations for future research to address the identified gaps in athletes’ and classifiers’ understanding of classification. Lastly, through the provision of practical recommendations, this work may support Para sport practitioners in improving athletes’ and classifiers’ experiences with classification.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".