Varieties of Embeddedness: Essays on Technological Transition in Automotive Regions
Bibliographic record
Abstract
Automotive incumbents are undergoing a technological transformation and face intensified competition from big tech companies. New entrants like Tesla are challenging existing automakers and betting on electric vehicles. Established software giants like Google's Waymo promise autonomous cars in the future. So, it could be either incumbents or big tech that dominate innovation activities and integrate autonomy, connectivity and battery technologies in vehicles. And their innovations may be geared toward reducing vehicles' use or toward preserving the car-centric paradigm. The dominant network of innovators can also differ across regions. In other words, the transition can have varied effects on the economic structure and mobility patterns in automotive jurisdictions. This dissertation aimed to identify how automotive jurisdictions reconfigure their industrial and support structures to promote new path development toward connected and autonomous vehicles (C/AVs). In political science, the Varieties of Capitalism literature is considered helpful in providing insights into how regions will adjust to these challenges. It suggests the national institutional context will shape the capitalist variety of technological change. However, transition in automotive jurisdictions diverges from these expectations. Therefore, this dissertation draws on recent conceptual advances from evolutionary economic geography to develop an analytical framework that casts light on how regional preconditions underpin different routes of transformation. The framework is applied to a qualitative comparative analysis of industrial path development towards C/AVs in two automotive regions, namely Ontario (Canada) and Baden-Wurttemberg (B-W). Findings suggest that in regions where automotive incumbents have historically dominated firm networks and been the main target of institutional supports, like B-W, automotive firms capitalize on the initial favourable conditions and lead innovation in C/AVs. When information technology (IT) firms are more influential in the automotive jurisdiction, like in the case of Ontario, there is more significant room for new entrants and innovation that challenges the car-centric paradigm. This has opened a window of opportunity for the Canadian automotive region to upgrade from a production hub to an R center. In summary, the dissertation shows that national-level institutions are limited in their ability to help us understand the processes of value capture and innovation at the sub-national level.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".