Developing Evidence-Informed Recommendations for the Management of Parasport Classification Using the AGREE II Instrument
Bibliographic record
Abstract
Sport administrators hold responsibility for implementing and managing parasport classification systems within national sport federations, yet they have called for guidance on how best to learn about and manage classification. This paper describes a consensus-based process, informed by the Appraisal of Guidelines for Research and Evaluation II instrument of developing novel, evidence-informed recommendations for the management of classification by national sport federations. A consensus panel (N = 8) reviewed extant research on classification, shared first-hand knowledge and expert opinion of the subject, and formulated the recommendations. Seven recommendations resulted, each accompanied by specifics such as who, how, and when to implement them. External knowledge users (N = 37) provided additional feedback on the draft recommendations. These recommendations serve to advance the systematic management of classification across Canadian national sport federations. Additionally, this work provides guidance on how to utilize the Appraisal of Guidelines for Research and Evaluation II Instrument when collaborating with parasport practitioners, thus bridging the gap between knowledge creation and implementation.
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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 | 0.469 | 0.667 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".