Assessing the Functional Dynamics of Ontario’s Electric Vehicle Technology Innovation System Under the Current Niche-regime-landscape Structure
Bibliographic record
Abstract
<p>Reducing transportation emissions is one of Ontario’s goals to tackle climate change. This thesis research analyzes the transition to electric vehicles (EVs) by using the technology innovation system (TIS) and the multi-level perspective (MLP) approaches to assess the functional dynamics of Ontario’s EV TIS under the current niche-regime-landscape structure. Although Ontario has outstanding domestic auto industry capability, the diffusion of EVs is relatively low. However, the analysis shows that Ontario might be on track to achieve the goal of light-duty vehicle emission reduction by 2030 due to the potential transition to the mass EV market. Entrepreneurial initiatives and the exogenic-driven legitimacy functions are recognized as the main motors to reinforce the functional dynamics and development of Ontario’s EV TIS. However, the lack of robust supply and demand-focused policies to open and lead the market to entrepreneurial enterprises and to innovate the status quo of the established auto industry regime in a timely manner may inhibit the formation of competitive EV TIS in Ontario.</p>
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".