Informally Self-Employed in Russia: Attitude to Formalization (On the Example of Saint Petersburg)
Bibliographic record
Abstract
The paper examines the attitude to the formalization of informally self-employed in Russia on the example of the city of St. Petersburg. The authors proceeded from the position that this social group is heterogeneous, and different characteristics of representatives of this social group affect the attitude to the formalization of their economic activity. The negative attitude to formalization of representatives of this social group was revealed on the surface. However, this negative attitude among different subgroups of informally employed people turned out to be different. The results of the study show that different age groups of informally self-employed people react differently to government initiatives regarding registration of such activities. The presence or absence of social status in the sphere of formal employment, which many self-employed people combine with informal economic activity, proved to be a significant social characteristic in forming the attitude of the informally self-employed to formalization. Thus, the great value has stability of the institutional framework of formal self-employment generated by the state, and the state’s determination to follow its promises given to informally self-employed, so that this social group formalized its economic activity. It was found that a fairly large proportion of the informally self-employed took up a waiting attitude towards the state’s initiatives to formalize the economic activities of this social group. This paper will be useful for representatives of Russian state authorities who are developing measures of socio-economic policy in relation to informally self-employed citizens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".