International Research Workshop «Foresight and Science, Technology and Innovation Policies: Best Practices»
Bibliographic record
Abstract
A Research Workshop «Foresight and Science, Technology and Innovation Policies: Best Practices» organized by the HSE Institute of Statistical Studies and Economics of Knowledge took place at HSE on October 13-14, 2011. The meeting was coincided with the two hallmark events: firstly, the creation on the ISSEK basis of the two international laboratories conducting research in the field of S&T and economics of innovation, respectively, and secondly, the formation of the Expert Group on Innovation Policy aimed at preparing proposals to adjust Strategy – 2020 for the Russian Federation. So far the workshop was focused on presenting interim results of the activities of the mentioned teams. Presentations were made by the representatives of the Manchester University (UK), Ottawa University (Canada), Georgia Institute of Technology (USA), OECD, UNIDO, the Netherlands Organisation for Applied Scientific Research, Malta Council for Science and Technology, the Russian Venture Company, Higher School of Economics as well as other organisations. The main discussion topics included: Foresight — policy issues and instruments; best national and international Foresight practices; applied Foresight; prospective innovation policy for the Russian Federation; STI policy instruments; new challenges for STI policy.
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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 | 0.050 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".