Bibliographic record
Abstract
Participation in sport on all levels, physical activity, leisure and recreation is seen as important for disabled people but at the same time barriers are reported for disabled people. Sports Pedagogy, kinesiology and physical education are fields that cover physical activities. The ability of the body is at the centre of many barriers to physical activities. The supercrip and ableism are two concepts used to question the narrative around the able body. Technologies (existing, envisioned and appearing) play an increasing role in the discussions of the able body including the ability expectation of a body with beyond species-typical abilities which could lead to new barriers to participation in sport on all levels, physical activity, leisure and recreation. In 2016, the first Cybathlon which labels itself as the “Cyborg Olympics” for physically disabled athletes took place. The 2024 version has the arm prosthetic race, assistance robot race, vision assistance race, brain computer interface race, exoskeleton race, wheelchair race, leg prosthetics race and exoskeleton race. Useful concepts to discuss the techno-influence are techno-supercrip, techno-poor disabled, techno-poor impaired, enhancement (transhumanized) version of ableism, technoableism and technowashing. The aim of this study was to ascertain how academic abstracts that cover participation barriers of disabled people in sport on all levels, physical activity, leisure and recreation and the discussions of these barriers within the fields of sports pedagogy, kinesiology and physical education cover barriers in conjunction with technologies, and the concepts of supercrip, superhuman, ableism, disablism, techno-supercrip, techno-poor disabled, techno-poor impaired, enhancement, transhuman, posthuman, (transhumanized) version of ableism , cyborg, technoableism and technowashing.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".