Bibliographic record
Abstract
This article analyzes timing and imaging systems used as sports decision aids (SDAs). Evidence of athletic performance in the form of timing and imaging data is the product of distinct interactions between humans, technology, and the live environment. As such, sports decisions are fallible. Yet the measurement of athletic performance is often presented as irrefutable thanks to enhanced technological precision. As this article shows, there are limits to the accuracy of timing and imaging systems as they are deployed in the physical environment, but such limits are rarely acknowledged in the public and professional discourse surrounding elite-level sport. To address this issue, the article analyses three sporting decisions: the 100 m butterfly race between Michael Phelps and Milorad Cavic at the 2008 Beijing Olympics; the third-place tie between Jeneba Tarmoh and Alysson Felix at the 2012 U.S. Olympic Trials; and the gold medal tie between skiers Tina Maze and Dominique Gisin at the 2014 Olympic games. The article examines the professional and public discourse surrounding each event as well as the regulations governing timing and imaging data in each sport to stress the situatedness and fallibility of SDAs. The article identifies limits to the accuracy of timing and imaging systems as they are deployed in the physical environment and calls on sports regulating bodies to clearly articulate the capabilities and limitations of timing and imaging systems in the production of evidence.
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 | 0.033 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".