UK-India collaborative study on low carbon technology transfer: Phase II Final Report
Bibliographic record
Abstract
This report details the findings of Phase II of the UK-India study on low carbon technology transfer. The study was conducted by SPRU (University of Sussex, UK) and TERI (India) between June 2008 and February 2009, with expert review from Margaree Consultants (Canada) and the Institute for Development Studies (UK). The study focused on three key issues: 1. The development of a decision making guide to help policy makers ensure technology transfer activities have maximum impacts on developing new technological capacity in recipient countries; 2. Further work on intellectual property rights (IPRs), including the development of policies that could help to overcome IPR barriers; and 3. Developing recommendations of how collaborative research, development, demonstration and deployment (R,D,D&D) initiatives between developed and developing countries might contribute to technology transfer. The study used a case study approach based on five low carbon technologies, namely: wind power; solar PV; hybrid vehicles; energy efficiency in small and medium sized enterprises (SMEs); and integrated gasification combined cycle (IGCC) for power generation. The emphasis throughout the study was on a consultative approach that engaged directly with industry, government and researchers, to yield grounded empirical insights and to raise awareness of the study amongst potential end users. About 200 people provided insights for this report. The majority of informants were based in India. However, where possible, discussions were also held with actors from the industrialized world.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".