Bibliographic record
Abstract
Pistorius, the Paralympic athlete, who wanted to compete in the Beijing Olympics but was initially denied to try out for the Olympics because his artificial legs were labelled a techno-doping device, is media story. Ever since Beijing, the main focus of the media has been around whether Pistorius should be allowed to compete against the normative able track and field Olympic athletes and whether his legs are indeed giving him an unfair advantage. This paper outlines other questions raised by the case of Pistorius that are not visible in the media. We contend that the current coverage of Pistorius misses the point and that there are issues to be dealt with that go beyond Pistorius as they generate problems for people with disabilities and, in the long run, also for the so-called people without disabilities.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.033 |
| Scholarly communication | 0.024 | 0.039 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".