Trust in golf distance measuring devices in users
Bibliographic record
Abstract
Whether to improve training or performance, the use of technology is increasing in sport and golf is no exception. A common piece of technology used in both competitive and recreational golfers is the Distance Measuring Device (DMD). Trust in technology can affect the choice to use technology and the performance of the human-technology team. The purpose of this research was to examine a golfer's confidence in their own abilities to determine yardage and trust in a DMD after a series of golf rounds both with and without the DMD. Thirty-three golfers with a handicap of 20 or less, and who typically used a DMD, participated in a repeated measures design study where measures were taken at baseline and following each of five rounds of golf. Trust in automation and confidence to estimate yardage without technology were assessed using a modified validated questionnaire on trust in automation (Jian et al., 2000). Results showed that trust in the DMD remained high throughout the study and did not change after golfers stopped using the device. Golfers' confidence in estimating yardage (without technology) did change over the course of the study where confidence decreased immediately after they played their first round without the DMD. However, their confidence increased again after playing another round without the device. There was no significant change in golfers' performance over the course of the five rounds. Future research should consider actual estimate accuracy and its relationship to self-confidence in one's own estimates without technology and trust in 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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".