Bibliographic record
Abstract
In today’s world, we are offered a constantly expanding number of technologies to integrate into our lives. We now utilise a range of interconnected technologies at work, at home and at leisure. The realm of sport is no exception, where new technologies or enhancements are available to athletes, coaches, scientists, umpires, governing bodies and broadcasters. However, this book argues that in a world where time has become a precious commodity and numerous options are always on offer, functionality is no longer enough to drive their usage within elite sports training, competition and broadcasting. Consistent with an actor-network theory approach as developed by Bruno Latour, John Law, Michele Callon and Annemarie Mol, the book shows how those involved in sport must grapple with a unique set of understandings and connections in order to determine the best combination of technologies and other factors to serve their particular purpose. This book uses a case study approach to demonstrate how there are multiple explanations and factors at play in the use of technology that cannot be reduced to singular explanations like performance enhancement or commercialisation. Specific cases examined include doping, swimsuits, GPS units, Hawk-Eye and kayaks, along with broader areas such as the use of sports scientists in training and the integration of new enhancements in broadcasting. In all cases, the book demonstrates how multiple actors can affect the use or non-use of technology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.455 | 0.263 |
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".