A Microscopic Analysis of the Relationship Between Prior Knowledge About Self-Driving Cars and the Public Acceptance: A Survey in the US
Bibliographic record
Abstract
Abstract Previous studies have shown that the level of awareness of SDVs is a deciding factor that affects the public attitude towards this emerging technology; however, none of these studies focuses on understanding the relationship between these two variables. Thus, this study utilizes a questionnaire survey with the objective of drawing the relationship between the public attitude and level of knowledge. A total of 2447 complete responses were revised from participants from the US. The results show that people with prior knowledge about SDVs are more likely to travel on SDVs. However, participants who know a bit about SDVs were the most likely to travel on SDVs when compared with participants who had no knowledge and participants who know a lot about SDVs. In addition, the analysis shows that the relationship between the level of knowledge and the level of acceptance of SDVs is not linear but rather parabolic.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".