Participation and Incentive Choice of Participants in an Early Vehicle Retirement Program in Quebec, Canada
Bibliographic record
Abstract
Early vehicle retirement programs are designed to reduce greenhouse gas emissions, improve air quality and traffic safety, and stimulate the economy by replacing the automobile fleet with vehicles that pollute less. The Province of Quebec's early vehicle retirement program also was designed as a mode shift tool by offering six incentives that included transit passes, rebates on bicycles and vehicles, and membership in a car-sharing cooperative. The characteristics associated with program participation and incentive choice were assessed. Between 2009 and 2011, more than 40,000 participants registered with the program and provided address, choice of incentive, age of vehicle, gender, and age. Census data provided measures of the built and social environments near participants' residences. The determinants of program participation rates were assessed with the use of Tobit regressions. Incentive choice was modeled with a multinomial logistic regression. Participation rates were high in low-density metropolitan areas and high-density nonmetropolitan areas as well as in areas with low unemployment rates, few immigrants, and young populations. Transit incentives were popular in dense metro politan centers with a large proportion of women, young people, and high incomes. Older participants were likely to choose car rebates, whereas bicycle incentives were associated with young male participants. Participants in low-income areas were likely to choose the cash incentive. The addition of incentive options that are feasible in low-density or low-income areas should be considered. Alternative transportation incentives are chosen primarily in dense urban environments where a mode shift is feasible.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".