Evaluation of a Population’s Migration Potential as an Important Component of Migration Policy
Bibliographic record
Abstract
Development of preventive migration state policy requires investigation of not only real but also prospective migration. This article provides the author’s methodological approach to the study of a population’s migration potential. The migration desires index (MDI), as one of the most important indicators of migration potential, was calculated for the unemployed urban population in Lviv, Ukraine, on the basis of the results of a monitoring sample survey (2013–2016, 2018). The MDI shows wave-like development dynamics. Generally, the share of “solid” migrants (persons who have firm plans to work abroad in the years ahead) grew from 14% in 2014 to 25% of the unemployed population in Lviv in 2018. Despite such a high level of migration desires, the respondents also showed a clear urge to be employed in Ukraine. Overall, the study results show that the improvement of employment opportunities in the national labor market and improvement of the wage system will contribute to a reduction of the level of migration potential and will thus slow the pace at which the working-age population is leaving. For those who still have a firm intention to go abroad, the state should provide an appropriate level of social and economic protection, primarily by establishing effective cooperation with countries that are most attractive for potential labor migrants. The author’s surveillance study shows that such countries are Germany, the USA, Canada, and Poland.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".