Innovation Offshoring and Outsourcing: What are the Implications for Industrial Policy
Bibliographic record
Abstract
This paper examines how innovation offshoring through global innovation networks affects Industrial Upgrading (IU) policies in Asia's electronics industry. I argue that developing countries cannot build their innovative capabilities by solely relying on their national innovation systems. For quite some time, these countries will have to draw primarily on foreign sources of knowledge as a catalyst for learning and capability formation. The paper discusses generic policy issues that host countries need to address to maximise the benefits of innovation offshoring. To leverage the potential benefits from global network integration, host countries must have in place vigorous policies to reduce the potentially high costs that may result from 'brain drain' (both domestic and international) when Trans-National Corporations (TNCs) are crowding out the local market for scarce skills, from the acquisition by TNCs of innovative local companies and from a potential deterrence effect of TNC labs on local R&D. I emphasise the critical importance of policies to develop strong local companies that can act as countervailing forces.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".