Regional Resilience and the Future of Ontario’s Automotive Sector in the Age of Digital Disruption
Bibliographic record
Abstract
The global automotive industry is currently experiencing the greatest disruption it has faced in over a century. The advent of connected, autonomous and electric vehicles and the popularity of ride sharing services are transforming the industry to one that is increasing referred to as transportation as a service (TaaS), transforming the customer experience and potentially shifting the entire industry sector away from private modes of transportation. Substantial uncertainty exists as to whether traditional automotive hubs in Automotive Alley will remain central to the growing digitization of the automotive industry or whether they will be replaced by new geographies with greater strength in digital technologies. This paper explores the extent to which efforts currently underway in the southern Ontario automotive cluster to meet the challenge of digitization in the auto industry are laying the foundations for a process of new path creation or modernization and institutional reconfiguration. The paper argues that the strength of Ontario’s regional innovation system (RIS) and growing OEM R&D investments provide expanding opportunities for the cluster to remain competitive either by 1) firms upgrading or moving up the value chain by strengthening skills and production capabilities; or 2) modernizing on the basis of connected or electric vehicle technologies or organizational innovations. The focus of the study are the current efforts on the part of OEMs to adapt to this rapidly changing technological paradigm and the role played by current federal and provincial policies to intensify regional knowledge linkages. The paper concludes with a discussion of the possible trajectory for the future development of the region’s automotive cluster.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".