New Path Development in a Semi-peripheral Auto Region: The Case of Ontario
Bibliographic record
Abstract
The automotive industry is facing disruptive trends and great uncertainty. The path forward for automotive jurisdictions is uncertain in terms of how automakers will allocate the production of new connected and autonomous vehicles (C/AVs). The introduction of C/AV technologies creates high levels of uncertainty both for individual firms and regional innovation systems (RISs). The intersection of established production competencies with emerging digital technologies raises questions about how regional pathways and RISs develop and how local and RISs adapt to changes in global innovation networks. Building on recent contributions to evolutionary economic geography (EEG), the article examines the impact of the current technology transition on Ontario’s automotive sector. Drawing on rich empirical data and recent conceptual advances in theorizing about new path development from EEG and the literature on global innovation networks, the article casts light on how the intersection between global innovation networks and regional actors is altering Ontario’s developmental path. It examines the potential for Ontario to diversify away from its historic status as a semi-peripheral automotive region with limited investment in research and development to one with a greater role in the emerging paradigm of connected and autonomous vehicles. The article explores the potential for path diversification based on interpath dynamics between the region's auto and information and computer technology sectors as well as the importance of both system-level and firm-level agency for altering the region's developmental trajectory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".