Frontier Technologies in Non-Core Automotive Regions: Autonomous Vehicle R&D in Canada
Bibliographic record
Abstract
For economically advanced locations, a primary response to deindustrialization has been to emphasize higher value-added activities, the target frequently being research and development (R&D). R&D tends to occur in locations proximate to corporate headquarters in general and the headquarters of global lead firms in particular. This pattern is especially evident in the automotive industry. Thus, for countries or regions lacking a targeted industry’s global lead firm, generating R&D is problematic. In the automotive industry, the introduction of frontier technologies—such as those supporting autonomous vehicles (AVs)—may reveal new patterns of R&D development, a consequence of firms engaging with innovation ecosystems disconnected from the traditional automotive industry and its headquarters-proximate geographic core or cores. This article explores these matters via a case study of Canada’s efforts to build an AV R&D profile. Canada does not host an automaker’s headquarters, but it does possess attributes that suggest it is well equipped to conduct such work. After constructing and analyzing a global database of patents related to AVs, this article demonstrates that Canada has contributed R&D focused on AVs at a rate above that which it has reached for automotive R&D overall. It also establishes that globally, even though AV-related R&D has emerged from non-traditional automotive locations, the preponderance of AV-related R&D is converging in core automotive locations: proximate to automakers’ global headquarters.
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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 | 0.004 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".