Transfer of Technology as an International Bridge for Sustainable Development: Issues for Developing Countries
Bibliographic record
Abstract
For equitable and appropriate measures needed for Sustainable Development, technology transfer works as a bridge in bilateral and multilateral agreements. The concept needs to clarify the Principle of Sustainable Development based on Common but Differentiated Responsibilities, accessibility and affordability, capacity building, resilience, adaptation as well as mitigation. Policies and Regulations regarding technology transfer discussed in the Montreal Protocol and Kyoto Protocol are significant. As climate change becomes the current problem, ESTs have drawn the attention of the UNFCCC. The fundamental challenges before the world are social disparities, degradation of soil, and depletion of water and natural resources. Technology transfer works as the platform for interaction between developed nations and developing nations to achieve Sustainable Development. A case study of Taiwan is reflected to explain the situation prevailing for countries that are not within the limits of the International Court of Justice or the UNFCCC. Overall, the transfer of technology has been the most crucial factor in maximizing trust and reducing tension to overcome the problem of climate change and other issues associated with it.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".