Value chains, networks and clusters: reframing the global automotive industry
Bibliographic record
Abstract
In this article, we apply global value chain (GVC) analysis to recent trends in the global automotive industry, with special attention paid to the case of North America. We use the three main elements of the GVC framework—firm-level chain governance, power and institutions—to highlight some of the defining characteristics of this important industry. First, national political institutions create pressure for local content, which drives production close to end markets, where it tends to be organized nationally or regionally. Second, in terms of GVC governance, rising product complexity combined with low codifiability and a paucity of industry-level standards has driven buyer–supplier linkages toward the relational form, a governance mode that is more compatible with Japanese than American supplier relations. The outsourcing boom of the 1990s exacerbated this situation. As work shifted to the supply base, lead firms and suppliers were forced to develop relational linkages to support the exchange of complex uncodified information and tacit knowledge. Finally, the small number of hugely powerful lead firms that drive the automotive industry helps to explain why it has been so difficult to develop and set the industry-level standards that could underpin a more loosely articulated spatial architecture. This case study underlines the need for an open, scalable approach to the study of global industries.
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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 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".