Bibliographic record
Abstract
In the previous issue we examined in detail the "image" of science that has emerged among the Russians at the beginning of XXI century. In this case, we presented relatively contradictory views of the widest strata of the population: on the need for state support of science on research priorities (economic development, improved healthcare and education, the environment and strengthening national defense); on unfairly low prestige of scientists as compared with other professions but at the same time on the positive attitudes to academic careers of their own children; about sluggishness of innovative behavior and the negative impact on him by the media. The four general attitudes - "paternalism", "faith in science", "technicism" and "syndrome of crumbling science." It should be noted that the "faith in science" approach is manifested in the form of strong scientistic positions and hopes for it in instrumental terms, but it is not supported by personal cognitive interest. We try to illustrate it in this publication. The article presents the results of six Russian representative opinion surveys conducted in 1995-2006. For international comparisons we use data from surveys implemented in the countries of the European Union, published in special Eurobarometer reports and materials from the report by the National Science Foundation, reflecting the results of similar surveys in the United States, Canada, Japan, Korea, China and Malaysia.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".