“It's not Fair!”: Multisensory reaction time differences between D/deaf and hearing populations in athletics starting systems
Bibliographic record
Abstract
When competing alongside hearing athletes in athletics, D/deaf athletes are typically faced with variable starting systems (e.g., lights, flags, vibrating armbands), which run concurrently with an auditory stimulus to encourage inclusion. However, a lack of consistency with regards to technologies adopted has led to inequitable opportunities for fast reaction times (RTs) between D/deaf and hearing athletes. Given that RT is a critical element of performance in sprint events, the aim of the current series of studies was to determine whether current starting systems present a disadvantage for D/deaf athletes. We tested RT differences between unimodal and bimodal - auditory, visual, and haptic stimuli across D/deaf and hearing populations in lab-based (Study 1) and field-based (Study 2) environments. Analyses for Studies 1 and 2 confirmed RT advantages for individuals able to access bimodal stimuli (e.g., hearing athletes accessing both auditory and visual stimuli). Findings support a visual-haptic configuration as the most equitable (and fastest) stimulus composition between populations. We then used semi-structured interviews (Study 3) to comprehensively explore the insights and experiences of existing starting systems from key personnel (e.g., athletes, coaches, NGBs). Three themes were developed using reflexive thematic analysis: (1) knowing when to go - experiences of starting systems; (2) organisational challenges; and (3) from awareness to action. Across the three studies, we provide insights into the limitations of current practice that culminates in a series of applied recommendations. Findings may be instrumental in informing UK Athletics and World Athletics policy regulations around starting systems to improve equity for D/deaf athletes. NOTE: For a British Sign Language version of the abstract scan the QR code.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".