What Would it Take for You to Stop Driving to Work? Employer-Based Transportation Demand Management at Brock University
Bibliographic record
Abstract
The prevalence of automobile dependency is a long-standing barrier to achieving a more sustainable future. To overcome it, cooperation between the private, public, and individual sectors is needed. The public sector has historically been the main agent of change in this capacity, and the role of the private sector is underrepresented even though employers have the potential to implement policies that can effect change. One sway is through the implementation of employer-based transportation demand management (TDM), which is a set of policies that can be used to influence the commute behaviors of employees. From the employees at Brock University, in St. Catharines, Ontario, we wanted to know: What would it take for you to stop driving to work? To this end, our research questions include: ‘How do Brock employees currently travel to and from work?’; ‘Which TDM policies would most and least likely encourage employees to consider using sustainable modes of transportation for their commute, if they do not do so already?’; and ‘What are the implications of this research for employer-based TDM strategies at Brock University and beyond?’ To answer these questions, we employed the use of an online stated preference survey, distributed over email and through campus-wide posters. Our results show that most of Brock's employees rely on their automobiles, and they may not yet be willing to give them up. However, they could be convinced to carpool and participate in mode-switch days, or to adopt more sustainable transportation habits with the help of shift flexibility. Most of all, we have shown that there is a possibility to enact policies that could effect change, even in an automobile dependent landscape.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 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) | 0.007 | 0.001 |
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".