International Research Workshop «Foresight and Science, Technology and Innovation Policies: Best Practices»
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it