The roles of science and technology in energy and environment research and development
Bibliographic record
Abstract
Countries are becoming increasingly aware of the importance of science and technology in relation to national development and the necessity of formulating a concise science and technology policy. The need to strengthen and orient the scientific and technological infrastructure in line with national development goals, through more effective use of an available qualified work force and the higher education system, is becoming widely recognized. Consequently, appropriate methods of assessing the impact of science and technology on national development are needed so that efforts are concentrated on areas potentially having substantial impacts. Numerous planning studies have been undertaken to this end, particularly by international organizations such as UNESCO, UNIDO, OECD and IEA. This study examines the inter-relationships of the disciplines of science and technology with energy and environment research and development (R&D) activities, particularly for developing countries. The connections between these topics are discussed along with some basic methods that can be used to exploit the relations. Some illustrative examples are presented. It is anticipated that the present study will serve as a preliminary step for more comprehensive work by providing an example of the utilization of formal methods in formulating science and technology policy for energy and environment R&D. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".