The Invictus Games Categorisation System: A framework for adapted sports
Bibliographic record
Abstract
Categorisation (also called classification) systems are a fundamental part of adapted sports. While current systems seek to promote fair participation, they may also conversely prevent individuals experiencing diverse and complex physical and psychological illnesses and injuries from engaging in adapted sports. Drawing on the experiences and perspectives of stakeholders working with the Invictus Games Foundation, this paper describes the Invictus Games categorisation system, an innovative framework for non-elite adapted sport competition that seeks to integrate individuals experiencing these diverse conditions. We review (a) the development of the categorisation system; (b) the categorisation process; and (c) the training of categorisers. The paper further highlights how these developments were influenced by recent advances in our understanding of the impact of pain-based impairments as well as psychological illnesses and injuries. Additionally, we present critical implications for organisations seeking to develop or refine categorisation systems to expand access to non-elite adapted sport competition. Our aim is for this paper to serve as a practical guide for stakeholders, researchers and practitioners.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".