The age structure of the scientific and pedagogical staff: data from the regions of Russia
Bibliographic record
Abstract
The purpose of our study was to evaluate the indicators characterizing the age structure of the scientific and pedagogical staff of universities and other higher educational institutions in the regions of Russia. In the course of the work, the indicators characterizing the specific weights of teachers belonging to five age groups from 25 to 34 years, from 35 to 44 years, from 45 to 54 years, from 55 to 64 years and over 65 years in the total number of teachers working in higher education institutes were evaluated. The study used official statistical information for 82 regions of Russia. We used the density functions of the normal distribution as models. The results of the research allowed us to draw conclusions: the largest number (more than a quarter) of teachers was observed in the group from 35 to 44 years; 24% of teachers were aged from 45 to 54 years; 20% of teachers were slightly older. The least number of teachers was observed in the age groups over 65 years and from 25 to 34 years. The proposed methodological approach and the results obtained have originality and scientific novelty, since the assessment of regional features of the age structure of scientific and pedagogical personnel in the regions of Russia has not been carried out before.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".