Bibliographic record
Abstract
Since the 1990s, Lithuania lost almost one-quarter of its population, and some regions within the country lost more than 50% of their residents. Such a sharp population decline poses major challenges to politicians, policy-makers and planners. The aim of this study is to obtain more insight into the recent processes of socio-spatial change and the role of selective migration in Lithuania. The main focus is on understanding who lives in those regions which are rapidly losing population, and who is most likely to leave these regions. This is one of the first studies to use individual-level Lithuanian census data from 2001 and 2011. We found that low socio-economic status residents and older residents dominate the population of shrinking regions, and unsurprisingly that the most ‘successful’ people are the most likely to leave such regions. This process of selective migration reinforces the negative downward spiral of declining regions. As a result, socio-spatial polarization is growing within the country, where people with higher socio-economic status are increasingly overrepresented in the largest city-regions, while the elderly and residents with a lower socio-economic status are overrepresented in declining rural regions. This paper provides empirical evidence of selective migration and increasing regional disparities in Lithuania. While the sociospatial changes are obvious in Lithuania, there is no clear strategy on how to cope with extreme population decline and increasing regional inequalities within the country.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".