Bibliographic record
Abstract
For successful social policy at different levels of society and the state, an objective assessment of the social situation and its impact on different groups of the population is necessary. In the Samara region, two-thirds of respondents aged 14-30 and 31-45 are primarily concerned about the problem of promising, well-paid work. For half of the respondents aged 46-60, this problem is in the 2nd place in terms of importance. This problem is also of concern to about half of the younger respondents and slightly more than half of the middle-aged respondents - this problem is on the 2nd place in importance for them. For two-thirds of respondents aged 46-60, the problem of limited financial opportunities is on the 1st place in importance. For about a quarter of respondents, the problems of "Getting any job" and "Lack of places where you can spend interesting leisure time" are considered relevant (3rd and 4th places). Thus, for a person, even in difficult socio-economic conditions, the problem of leisure is also important. Among the most notable (on the 5th place) is the problem of obtaining higher education, which is most relevant for young people. All problems (and not only those that have taken leading positions in opinion polls) require attention to themselves, taking into account the age characteristics and special needs of people of different generations.
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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".