Migracijska dinamika v Sloveniji po letu 2000 z vidika zunanjih in notranjih migracij ter regionalnih razlik
Bibliographic record
Abstract
This article analyses migration flows in Slovenia since 2000, detailing the patterns of various categories of migrants based on demographic and other factors (such as nationality, place of birth, region, gender, age, family migration history). It offers a periodisation of migration between 2000 and 2024 in light of socio-economic changes, particularly the financial and Covid-19 crises, both of which profoundly influenced migration dynamics and have radically changed the current demographic conditions as well as reshaped the Slovenian demographic landscape. Slovenia experienced a net loss of 57,284 Slovenian citizens while gaining 217,177 foreign nationals over the past quarter century (2000-2024). The author highlights internal population redistribution, increased emigration of Slovenian citizens from the periphery and border regions, and a concentration of foreign immigration in urban areas and the central region.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".