Elements of the Public Policy of Science, Technology and Innovation
Bibliographic record
Abstract
This work analyzes the structure, elements and formulation of science, technology and innovation policy, providing examples of countries on distinct continents. The authors show that the following elements can be used as the basis for analysis of national cases: institutions, legal framework, science policy agents, plans, programs, resources and assessment instruments. Key words: Science and technology policy; innovation; research and development; science policy agents; legal framework of science policyResume: Cet article analyse la structure, les elements et la formulation de la politique de la science, de la technologie et de l'innovation, en fournissant des exemples des pays dans de differents continents. Les auteurs montrent que les elements suivants peuvent etre utilises comme des bases d'analyse des cas nationaux: les institutions, le cadre juridique, les agents de la politique de science, les plans, les programmes, les ressources et les instruments d'evaluation.Mots-cles: politique de la science et de la technologie; innovation; recherche et developpement; agents de la politique de la scientifique; cadre juridique de la politique de la science
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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".