Is a robot car still a car? Consumer perceptions of fully automated vehicles and automobility in Canada
Bibliographic record
Abstract
Fully automated vehicles (FAVs) could transform private car-based mobility or “automobility”, but the direction of FAV impacts is uncertain and contingent on consumers. We investigate consumer response to FAVs and its relationship to automobility by conducting semi-structured interviews with 34 new car buyers in British Columbia, Canada. First, we assess consumer response through exercises where participants design their “ideal next vehicle”, choosing between FAV versus conventional vehicle (CV) options. We find that two-thirds of participants prefer FAVs over CVs in a scenario where FAVs are available for sale and include a steering wheel. Second, we conduct qualitative content analysis of transcript data to investigate consumer engagement with automobility upon access to privately-owned and shared FAVs. We apply a conceptual framework of consumer “automobility engagement”, considering preferences for car ownership and residential location, car use emotions, symbolic and societal perceptions, and social norms. We find that minorities of participants expect to own fewer cars or change residential preferences following access to FAVs. Results also indicate that FAVs largely reproduce the symbolic and social significance of car ownership. Contrasting with these results, several participants expect FAVs to reduce automobility impacts on society at large. We conclude that FAV adoption may reproduce existing consumer engagement with automobility and discuss implications for transport emissions and policy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".