METHOD OF REVEALING CREATIVE PERSONS IN SCIENTIFIC TEAM
Bibliographic record
Abstract
An approach is developed to quantify the creativity of scientific and pedagogical staff using the Russian Science Citation Index in terms of their professional performance, including scientometric indicators. The problems of conducting surveys and testing large audiences of respondents are analyzed. Variants of possible criteria and indicators used to assess employees’ professional activities, including identifying their creative contribution to the collective indicator, are considered. The concept of intellectual products of educational and scientific institutions is defined, since their creation, development and publication are one of the requirements for employees. The article shows the interrelation of the respondents’ personal intellectual abilities, including creative ones, and their contribution to the institution scientific potential. The authors substantiate that it is advisable for faculty members to use such categories as educational, methodological, scientific activities, advanced training. The interrelation of distributing the employees’ contribution to the institution scientific potential with the Gauss law is considered. The authors propose to apply the Pareto principle, according to which “20% of efforts give 80% of the result, and the remaining 80% of efforts is only 20% of the result” to assess the creativity level. About a quarter of the employees are proven to produce about three-quarters of the entire intellectual potential of the university or a scientific institution. A threshold equal to 0.44 is justified, according to which the Pareto principle is implemented when evaluating each employee’s contribution. The developed analytical apparatus and illustrative material revealing the essence of the developed method are presented. Conclusions are drawn and directions for future research are outlined.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".