Does length of ride, gender, or nationality affect willingness to ride in a driverless ambulance?
Bibliographic record
Abstract
Due to the frequent lack of ambulances and personnel, the purpose of this study was to examine consumers’ willingness to ride in an ambulance that was either driven by a human driver or completely automated (with no human driver) based on the gender of the participant and their nationality, either Indian or American. A two-study experimental design was utilized using over 1000 participants. In study 1, the length of the ride and the type of driver were manipulated, while in study 2, the length of the ride was manipulated across genders and nationality. Study 2 also collected affect measures to complete a mediation analysis. The findings indicate that consumers’ willingness to ride was significantly lower for longer rides when using the automated ambulance. There were significant interactions between nationality and gender and nationality, gender, and length of the ride. Affect was found to significantly mediate the relationship between willingness to ride and both nationality and gender. These findings are discussed in greater detail, along with recommendations for future research and limitations to the study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".