HSE Annual Conference on Foresight and S&T and Innovation Policy
Bibliographic record
Abstract
Foresight approaches have been widely used in addressing many problems in S&T and innovation policy, and the methods employed continue to be developed. Foresight practitioners agree that there is still considerable scope for innovation and improvement in the field, however. How can conceptual frameworks and tools of Foresight be advanced so to better contribute to policymakers' search for Great Responses to the many Grand Challenges that confront contemporary societies? These fundamental questions were in focus in discussions at the Annual Conference on Foresight and S&T and Innovation Policy held on late October 2013 at the National Research University — Higher School of Economics (HSE) by the HSE Institute for Statistical Studies and Economics of Knowledge. The following topics were addressed in the agenda: The role of Foresight in S&T and innovation policy; Networking and the use of Foresight results; Foresight for companies, sectors and technologies; Evolution of S&T Foresight. Presentations were made by renowned experts from international organizations (OECD, UNESCO, UNIDO), worldwide leading Foresight think tanks — Manchester Institute of Innovation Research, University of Manchester, UK; Korea Institute of S&T Evaluation and Planning (KISTEP); National Institute of Science and Technology Policy (NISTEP), Japan; University of Ottawa (Canada); Research Center for Futures Studies, University of Hawaii at Manoa, US; Institute for Technological Innovation, University of Pretoria, South Africa; Singularity University, US; Centre for Social Innovation, Austria, as well as from Ministry of Education and Science of the Russian Federation, HSE and a range of other organizations.
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.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.069 | 0.015 |
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".