Bibliographic record
Abstract
Wearable technologies' popularity in sporting practices continues to grow. Runners use GPS watches and activity trackers to track steps, log miles, map courses, and monitor heart rates. Likewise, wearables are integrated into long distance running events, with race officials relying on technologies to effectively execute events. However, technologies can also enable and monitor cheating. Many studies focusing on the individual explore why cheaters make unethical decisions. Actor-Network Theory shifts cheating's focus from the individual and moral failings to an assemblage that includes not only the runner, but nonhumans, such as technology, as well. A 2015 Canadian Ironman cheating incident case study illuminates intricate relationships and networks between humans and nonhumans. By examining the intersections of cheating and technology in running sports, the authors see where and how technology works as intended or is repurposed. Whereas a human-centered approach to sport and cheating dismisses wearables' agency, Actor-Network Theory reveals their underexamined, sociotechnical complexities.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 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".