Potential Migration Investigation in the Mechanism of Labor Market Regulation
Bibliographic record
Abstract
Effective regulation of labor market and elaboration of preventive policy measures requires proper information support. Such support can be provided by the investigation of not only real but also potential migration. This article provides the authors’ complex approach to the study of a potential migration. In particular, three stages of potential migration are investigated on the basis of the results of a panel sample survey of unemployed in Lviv city, Ukraine (2013–2016, 2018-2019): migration desires, plans (decision) and preparations. Thus in 2019 the share of respondents having positive migration desires made up 56%, planning to move abroad – 26% and only 18% made some preparations for moving. Based on the results obtained during six years of study a map of migration preferences is made. So Germany, the USA and Canada are mostly chosen for permanent residence or long time migration. Poland and Germany are the most desired for temporary work. Based on the logistic regression model the impact of gender and age on decision regarding employment abroad is showed. Respondents’ estimations of their financial situation and employment opportunities in relation to their potential migration are also analyzed. Presented in the article study may be replicated in other regions and other samples may be used for survey. It would allow comparative analysis of potential migration between different groups and regions and would be helpful for policy making.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".