Why Industry 4.0 is not enhancing national and regional resiliency in the global automotive industry
Bibliographic record
Abstract
The post 2000 period has witnessed the rise of countries offering low-cost labour as important hubs for automotive manufacturing. As that occurred, automotive 'semi-periphery' countries faltered: struggling to retain vehicle production, unable to obtain mandates for more knowledge-intensive aspects of automotive value chains. For them, Industry 4.0 (I4.0) is considered an ideal tool to enhance competitiveness. That is because even though they have high labour costs and lack a homegrown automaker, they do have well-educated workforces. Here, we examine the technological upgrading strategies of manufacturers in a prototypical semi-periphery location: Ontario, Canada. We find that few firms there are making investments in I4.0-oriented technologies sufficient to upgrade their position within global production networks (GPNs). Consequently, notwithstanding the prominence of I4.0, our results indicate that I4.0 is unlikely to spur economic resilience in automotive semi-peripheries. Even so, targeted deployment of industrial policy measures may augment I4.0's applicability in those locations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".