Technology driving changes in competitor decision making and match management
Bibliographic record
Abstract
The main aim and focus of this work is to examine the impacts of the differing information technologies, currently used in sport, are having on the competitor's decision making processes and their match management. One Olympic sport which is currently introducing mandatory decision making technologies to the scoring processes was examined through discussion groups and interviews. The participants in the study had all participated and competed from the grassroots level to the elite level in Taekwondo, and exposed to various 'officiating' technologies as the technologies were being adopted by their sport. The findings present that, in the majority, the differing degrees of information technology being employed does impact on the athlete's decision making process and the individual's match management. When information technology is being used to provide a mechanism to ensure the correctness of officiating decisions, the outcomes of the matches can be additionally affected by the athlete's ability to adapt to the technology as well as the situation. The diligent use and application of appropriate technologies can be used as an effective aid, but it does come at a cost. Since the introduction of the use of decision making technology, the way the athlete prepares and competes in a match has changed. The findings provide a basis for further studies and examination of the impacts of the introduction of information technologies into other sports and as a transformer of sport.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".