Social and municipal influences on intention to purchase electric and hybrid electric vehicles in London Ontario, CA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We conducted a case study in London, Ontario to identify factors that influence decisions to purchase low carbon vehicles including what role municipal governments might play in encouraging low carbon vehicle purchase decisions. As part of a city-university partnership, this study reports (n = 257) results from a mail-out survey. We test mainly whether social influences and mechanisms under municipal control predict intent to purchase electric vehicles (EV) and hybrid electric vehicles (HEV). Both proximal social influencers (family and friends) (.179**, .393**) and distal social influencers (.219**, .142*) predict intent to purchase EV and HEV respectively. City information sessions (.161** EV) and City promotion (.141* HEV) significantly influence intentions, while City-provided EV parking and charging are not. While municipalities may find other areas with greater impact on GHG reductions, the findings support promoting the social aspects of EV and HEV purchasing and providing relatively low-cost promotion/events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it