On the Electrification of Canada's Vehicular Fleets: National-scale analysis shows that mindsets matter
Bibliographic record
Abstract
The MCmaster Institute for Transportation and Logistics and its partners, including the University of Windsor, have been carrying out research examining the unfolding transition toward electrified transport in Canada. A premise underlying the work is that the tremendous progress on the technological side of electrification (e.g., reduced battery costs) is such that many of the remaining primary barriers to increased electrification are not really technological in nature. The main focus of the work, then, has been to gain a better understanding of factors in varying adoption contexts that may influence the rate at which electrification takes place. Understanding the potential consumer of EVs has been a central focus, but two additional survey efforts, discussed more fully later in this article, have examined adoption perspectives in governmental and corporate fleets. With regard to public transit fleets and the prospects for electric buses (e -buses) in Canada in particular, a cross -national tour was undertaken to conduct in -person, semistructured interviews with leading municipalities/ transit operators. Insights from this effort are also covered in a following section.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".