Towards Sustainable Attendee Transportation: Modeling the Psychological Determinants of Reduced Car Use to Special Events
Bibliographic record
Abstract
Characterised by extensive car use, attendee transportation has been recognized as the largest greenhouse gas contributor of a special event. Current strategies towards reducing car use, however, have been met with varying success due to a limited understanding of the factors affecting attendee mode choice. Therefore, this thesis sought to identify the extent to which the psychological determinants of behaviour, as set out in the Theory of Planned Behaviour, Theory of Interpersonal Behaviour, and Norm Activation Model, are associated with special event attendee non-car use. Through a quantitative survey of 500 Canadian panel participants and PLS-SEM, this study evaluated the three theories independently and proposed a combined model. Findings indicated the significance of psychological determinants, across all three theories, in predicting attendee non-car use. Thus, in addition to ensuring the availability and accessibility of sustainable transportation alternatives, organizers should focus on implementing ‘soft’ interventions, designed to encourage sustainable attendee mobility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".