The post-Yugoslav space on a demographic crossway: 25 years after the collapse of Yugoslavia
Bibliographic record
Abstract
The aim of the article is to integrally assess the demographic changes after 1990. It is presumed that considerable variations exist in terms of intensity of demographic processes. The following analyses encompassed the relevant national or regional statistics, employing methodological adjustment in order to enable data comparison. Herewith, the changing definitions of population presented a special analytical problem. The so-called principle of ?permanent? residence was largely replaced with the principle of ?usual? residence. By way of the usual residence it was possible to single out the present population and thus to approach the analysis. The main goal was to assess the direct and indirect demographic loss within the post-Yugoslav space. The combined analysis showed that the whole post Yugoslav area suffered a loss of about 5 million inhabitants (including the permanent emigration of the former guest-workers). Except from Slovenia, and stagnating Montenegro and Macedonia, all other countries from the Yugoslav space have lost more or less of their population. Losing a quarter of its pre-war population, Bosnia-Herzegovina suffered the most (1.093 million), but the high loss was determined also for the neighboring Serbia and Croatia. Serbia within its pre-war territory lost almost a million or one tenth of its population, while Croatia lost more than half a million or one ninth of its population. The three core Yugoslav areas lost more than 2.5 million. The analyses confirmed the striking regional differences as well. The highest relative depopulation was recorded in Republika Srpska of Bosnia-Herzegovina.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".