Main directions and support measures for scientific, technological and innovative policies in the world during the COVID-19 pandemic
Bibliographic record
Abstract
A scientific study of the directions for the formation, implementation and support of scientific, technological and innovation policy (STI policy) in the highly developed countries to overcome the COVID-19 pandemic and the crisis phenomena has been investigated. Measures and instruments of general political support for the STI sphere, in addition to effective medical and pharmacological support, also cover organizational and technical, financial, socio-economic, information, scientific and innovative support. Particular attention is paid to the analysis of directions for supporting scientific research, the development of new technologies and innovations to overcome the consequences of the coronavirus; coordination of actions and strengthening of cooperation at the national and international levels. The features of the introduction of state assistance instruments (support packages) of innovative business structures during the crisis are considered; holding collective events at the national and international levels; open exchange of data on the results of research and development — are analyzed. OECD data on decisions and measures taken by national governments and international organizations to overcome the pandemic, as well as support scientific and business structures during the crisis, have been analyzed and systematized. Eight main directions of STI support for five countries (Great Britain, Germany, Canada, Norway, Japan) are highlighted, which are combined in a summary table.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".