'The flea on the tail of the dog': power in global production networks and the restructuring of Canadian automotive clusters
Bibliographic record
Abstract
Recently, studies of industrial clusters and global production networks (GPNs) between large transnational corporations (TNCs) and smaller firms have focused on how power differentials shape, and sometimes undermine cluster innovation and governance. Such studies raise issues of how to conceptualize TNCs power within GPNs and some economic geographers have adopted approaches that seek to integrate the post-structural insights of actor network theory (ANT) with heterodox and Marxian value theory. Based on a case study of the Canadian automotive industry, we engage in a sympathetic critique of this perspective via a realist position that distinguishes between structural and actual power. We argue that differences in structural position derived from financial size matter, although do not necessarily determine actor network relations. Yet, TNC Original Equipment Manufacturers (OEMs) have a tendential actual or ‘power over’ smaller automotive suppliers due to superior financial resources, their strategic position within GPNs and especially their relationship with state accumulation projects designed to capture those segments of GPNs, which offer the greatest potential for the creation and enhancement of value. We show that such projects were critical to the development of the Canadian automotive industry and the Kitchener and Windsor, Ontario automotive clusters. Although large firms were generally favoured by such policies, suppliers and SMEs were able to partially offset power asymmetries through innovation, often by accessing informal local networks in clusters. More recently, however, smaller component firms and the coherence of these clusters are being threatened by neo-liberal Schumpterian Competition State policies that privilege OEMs and larger firms and by the restructuring of automotive GPNs in response to overcapacity and falling profits.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".