‘Third era’ parasport athletes and athlete ‘volunteer’ appearances (AVAs): a relational analysis
Bibliographic record
Abstract
In the ‘third era’ of Paralympic sport (Howe, Citation2008), there has been a discernable shift in focus towards high performance outcomes, fuelled by dramatic increases in funding and medal expectations. With these increases there have been significant implications for the lived experiences of Parasport eligible disabled athletes. This shift has facilitated more freedom and allayed some of the concerns/energies required to facilitate dual-career activities and in turn, allowed for greater commitment to their Parasport careers. However, as this paper will explore, there are other aspects of Paralympic athletes’ lives influenced by this development that remain under explored – including the fact that classifiably eligible disabled athletes are required, as are their able bodied peers, to contribute to schemes where their role as a visible asset is harnessed in the form of athlete ‘volunteer’ appearances (AVAs). In response, this paper draws upon the relational theorising of Crossley (Citation2011) to analyse data from a multiple case study approach in order to consider the implications of an array of athlete ‘volunteer’ appearances (AVAs) performed by nine Paralympic athletes across a range of sports.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".