Scoping review of literature and systematic search of web-based resources: parasport classification instructions, experiences, and outcomes
Bibliographic record
Abstract
While classification is essential to parasport, members of the Paralympic Movement commonly report having limited access to information on the topic. Despite increased academic interest in recent years, the total body of research on classification has not yet been mapped or documented. To assess the breadth of literature on classification, we conducted a scoping review following the protocol set forth by Arksey and O’Malley (Citation2005). Additionally, we completed a six-step systematic online search as outlined by Stansfield et al. (Citation2016) to document the type and quality of information on classification available outside bibliographic databases. The most frequent topics discussed in the literature were (a) coach/classifier roles during classification, (b) athletes’ perspectives on classification, and (c) the influence of classification on athletes’ participation (e.g. on social dynamics, athlete development). Webpages reviewed were of relatively low quality, with <50% reporting authorship, references, or a statement of disclosure. Webpages reviewed were primarily produced by national/international sport organizations and provided more professional (i.e. technical) information on classification than inter-/intrapersonal information or instruction. This review provides insight into the type and quality of classification knowledge available to parasport participants and may inform future research and practice related to parasport at all levels.
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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 | 0.090 | 0.260 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.077 | 0.054 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".