Asia's 'Upgrading Through Innovation' Strategies and Global Innovation Networks: An Extension of Sanjaya Lall's Research Agenda
Bibliographic record
Abstract
This essay is a very personal homage to Lall’s work on technological change and industrialization, in particular his pioneering study on technological capabilities, prepared for the OECD Development Centre (Lall, 1990). I will not seek to add yet another review of Lall’s work. Instead, I will sketch a roadmap for extending Lall’s research agenda to explore the challenges that result from global transformations (i.e. global innovation networks) for Asian attempts to upgrade its industries through innovation. I will demonstrate that Lall’s framework remains valid, once globalization extends beyond markets for goods and finance into markets for technology and knowledge workers. As a result, Asia’s integration into global production networks is now complemented by its integration into global innovation networks, which adds a new dimension to Lall’s research agenda. If not mentioned otherwise, the evidence used to support my arguments draws on unique data base of global innovation networks for a sample of now almost 150 companies in the information and communications technology industry (Ernst, 2008b) . Section one of the essay introduces what I consider to be the essence of Lall’s work, summarized in five propositions. In the rest of the paper, I will use these propositions to study the link between ‘upgrading-through-innovation’ strategies and global innovation networks. In section two, I introduce a conceptual framework to examine how specialization, learning and innovation enhance the potential for industrial upgrading. Section three addresses the international dimension of industrial upgrading – I discuss characteristics and drivers of global innovation networks and explore implications for learning and knowledge diffusion. Section four presents generic policy suggestions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".