Modeling of Social Risks in the Labor Sphere
Bibliographic record
Abstract
Modern society has developed in such a way that social reality is characterized by the significant dynamics of all processes and their uncertainty. Under such conditions, risk accompanies any purposeful activity of the social subject, and, in turn, the latter is aimed at reducing the uncertainty of its results. The purpose of this paper is to form the basis of a comprehensive study of social risks in the labor sphere and to develop practical recommendations for minimizing their negative consequences. In order to determine the main factors influencing the probability for the unemployed not to work in the specialty in which they have trained, we used the data of a micro-level survey on economic activity of the population to build linear regression models based on structural variables. As a result of applying the method of economic-mathematical modeling, in particular the basics of probability theory, the models of social risks of unemployment in terms of occupational groups and employment of unemployed persons outside of the specialty they have trained in were developed. The models developed made it possible to formalize and identify patterns of supply and demand dynamics of labor in terms of professions, as well as to identify the main factors influencing the change in the probabilistic characteristics of employment of unemployed persons outside of the specialty they have trained in.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".