Bibliographic record
Abstract
Abstract The drivers of economic globalization are leading many firms to disaggregate and redistribute their operations by outsourcing and offshoring. The result of the process is to create global value chains (GVCs) that are a collection of loosely affiliated, spatially distributed firms engaged in bringing products from raw materials to end use. A key insight from previous research is that GVCs are typically orchestrated by multinational enterprises (MNEs) given their control over key markets or critical technologies. Yet, very little is known about the emerging phenomenon in which MNEs appear to control production along the GVC without ownership of those assets. This is an important issue as consumers, regulators, and civil society are holding flagship MNEs increasingly responsible for behavior and performance along their entire GVC. This chapter analyzes GVC governance to highlight the fact that MNEs often require specific types of capabilities that relate to the context of their industry and the GVC in which they are embedded. The dynamic capabilities approach is extended to explore the ways and means of GVC governance by lead MNEs to shed new light on the contextual differences that influence the resources and capabilities required to improve GVC performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".