The Impacts of Disruptive Technological Change in the Southern Ontario Automotive Parts Industry
Bibliographic record
Abstract
In response to recent challenges facing the Southern Ontario automotive industry, including disruptive changes in vehicle product technologies, federal and provincial Canadian governments have sought to transition the sector’s strategic focus from manufacturing to innovation and new product development. In doing so, they have established numerous innovation programs and formulated a supercluster strategy designed to enhance levels of corporate automotive research and development (R&D). Consequently, several observers suggest that the Southern Ontario automotive industry has embarked on a new developmental path. Employing the GPN 2.0 approach, this study evaluates these claims and the efficacy of current government policy by examining the regional-level impacts of recent technological advancements in the Southern Ontario automotive parts industry. A 2015 plant-level survey of parts producers competitive and innovation strategies and corporate interviews are used to investigate the extent to which the sector is adapting current products and processes, at what scale automotive innovation occurs and the location factors which attract new automotive technology investments. Three primary research findings follow from this analysis. First, regulatory, technological and safety concerns are inhibiting industry advancement and supplier investment in components for electrical propulsion and CAVs. Second, few auto parts producers engage in early-stage innovation or compete based on product differentiation. Third, global parts suppliers continue to locate their primary corporate R&D facilities outside of the province. Subsequent chapters explore the implications of these findings for economic development in Southern Ontario. Corporate interviews reveal that automotive Original Equipment Manufacturers (OEMs) and global automotive suppliers shape which early-stage R&D investments are made, where associated automotive innovation activities occur, and how new product design and engineering information is transferred within the sector. The overwhelming majority of new technology investments are observed to cluster in Michigan and serve to reinforce Southern Ontario's interconnection with the Great Lakes automotive region (GLR). Due to built-up manufacturing skill and expertise in the province, many parts suppliers continue to transfer product specification information both to their own plants in Ontario and those of their lower-tier suppliers. The concluding chapter identifies three pillars upon which the resiliency of Canadian automotive parts manufacturing can be secured.
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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 | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".