Recasting ‘truisms’ of low carbon technology cooperation through innovation systems: insights from the developing world
Bibliographic record
Abstract
A key challenge of approaches to low carbon technology transfer/cooperation is that too much attention is placed on outcomes, neglecting technology cooperation processes. An innovation systems (IS) analytical lens can help to understand dimensions of what makes low carbon technology cooperation more effective, as IS emphasizes the importance of these technology processes. In developing countries, IS analysis tends to focus on activities of firms, the public sector and universities (also coined the triple helix) aimed at improving the quality of ‘hardware’ while lowering the costs of production. While important, these aspects constitute partial segments of IS. This paper therefore advances the concept of IS within developing countries in the following ways. This paper questions the assumption that these IS are absent and that producer–user interaction is weak, through unpacking the notion regarding who is innovating and what is low carbon innovation. In doing so, we capture the roles of alternative actors (e.g. lay people versus only experts), and activities and products (e.g. ‘improvised’ goods and processes versus frontier, or second-tier, technologies) within these systems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".