Potential Migration Investigation in the Mechanism of Labor Market Regulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it