The Paralympic Games: from a rehabilitation exercise to elite sport (and back again?)
Bibliographic record
Abstract
Background: The Paralympic Games, and their forebears the Stoke Mandeville Games, grew out of the rehabilitation of spinally injured military personnel at Stoke Mandeville Hospital, UK. Even though the Paralympic Games are rooted in a rehabilitation background they are now the second largest elite multi-sport event in the world after the Olympic Games. Content: Despite this move away from a rehabilitation model to an elite sporting model programmes have recently been introduced in Australia, Canada, the United Kingdom and the USA that use sport as an integral part of the rehabilitation of soldiers injured in current conflicts. These programmes are directly linked to the National Paralympic Committee (NPC) of each country and in some cases these soldiers are fast-tracked into that nation's Paralympic training programmes. This paper will look at some of the reasons why disability sport and the Paralympic Games have become so important and outline the role that they play in both rebuilding lives and as a form of social (re)education. Conclusion: The growing acknowledgement of the impact of sports participation upon the psychological and physical wellbeing of people with disabilities combined with the impact of disability sport in general and the Paralympic Games in particular upon non-disabled attitudes towards disability and the increasing number of soldiers severely injured in battle have conspired to re-new the link between injured military personnel and the Paralympic Games.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".