"That yardage can't be right? ": Trust in golf dmds in non-users
Bibliographic record
Abstract
Whether to improve training or performance, the use of technology is ever increasing in sport. A common piece of technology used to aid golfers is a distance measuring device (DMD). The purpose of this study was to determine how the introduction of a DMD to golfers who do not currently use a DMD affected their trust in DMDs, their confidence in estimating yardage and their golf performance. Eighteen golfers with a handicap of 20 or less participated in a repeated measures design study where measures were taken at baseline as well as following each of four rounds of golf. Trust in automation and confidence to estimate yardage were assessed using a modified validated questionnaire on trust in automation (Jian et al., 2000). The participants took baseline measures of demographics, experience and trust in abilities at an initial meeting. Following this meeting, they played two rounds of golf without a DMD, completed the trust in abilities measure and reported their golf score following each round. The participant then played two more rounds of golf using the DMD, completed both the trust in abilities and trust in automation measures, and reported their golf score following each round. The introduction of DMDs to non-users significantly increases their trust in automation, significantly decreases their confidence in their own ability to estimate yardage, and did not improve or decrease golf performance. Future research should examine trust and confidence levels in current DMD users and examine if DMD use impacts performance for golfers with greater ability.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".