All for one and one for all? Integration in high-performance sport
Bibliographic record
Abstract
Purpose : To examine the lived experiences of the integrated model of elite sport from the perspective of parasport insiders.Research design : Since 2002, an integrated model for competition has been implemented by Commonwealth Games Federation (CGF). Commonwealth Games XXI was selected as a relevant location to conduct research regarding integration. Ethnographic methods included interviews, go-along conversations and observation. Interviews were conducted with Games personnel to enhance understandings of integration in sport. Qualitative thematic analysis was conducted to determine significant themes.Findings : The findings confirmed that integration is considered the preferred model among non-athletes who were unanimous in their support. Analysis indicated that the parasport athlete experience was mixed, reflecting negative and positive experiences. Three themes were identified as impactful of the athlete experience: ableization, size of the parasport contingent, lack of unified collective voice.Practical implications : Future research that examines whose assumptions inform decision making and program delivery is necessary to determine the merits and drawbacks of integration in sport.Research contributions : By foregrounding the lived experiences of a group of parasport insiders, the research generates unique insights into the integrated model of sport from a perspective that is often missing, and credibly informs those who manage integration in sport.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".