Exploring “automobility engagement”: A predictor of shared, automated, and electric mobility interest?
Bibliographic record
Abstract
Automobility theory investigates the prevalence of the privately-owned car, including technology, infrastructure, and cultural elements. In an application of this theory, we quantitatively explore consumer engagement with aspects of automobility related to car ownership and use. We identify seven potential constructs of “automobility engagement” that might help explain consumer interest in shared, automated, and electric mobility. We develop 40 questionnaire items based on a literature review and analyze survey responses from a representative sample of 3,658 Canadian respondents. First, we conduct exploratory factor analysis and identify seven factors, such as “Car Identity” and “Societal Concern”. We then explore the role of these factors in consumer interest in ride-hailing, carsharing, fully automated vehicles, and electric vehicles through regression analyses. We find that “Societal Concern” predicts interest in all innovations but carsharing, while other factors are more specific. We conclude that quantifying automobility engagement can help to understand consumer interest in innovations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".