THE POSSIBLE AND NEGATIVE EFFECTS OF THE POPULATION’S MIGRATION PROCESS IN MACEDONIA AND THEIR REFLECTIONS IN THE BUDGET OF THE COUNTRY
Bibliographic record
Abstract
This article focuses on the very current social phenomenon of the territories where we live today, called the migration of the population. The migration processes of the population in Macedonia and their reflection on the country's budget, the country's economic situation, the economic situation, the causes of migration, the economic consequences of the state, state benefits, internal migration and dealing with migration as an unstoppable process are the points discussed and developed in the paper.Migrations exist from the very early existence of the human race, as throughout history, man has migrated uninterruptedly, even over long periods of time, only to provide better living conditions. Even though many things have changed from thousands of years ago, there are still many reasons why people choose to migrate to other countries. They could be economical, political, or geographical reasons.Nevertheless, migrations in today's Macedonia have a long history, damaged by various occupiers such as the Roman, Byzantine, Ottoman, Yugoslav, etc.Because of the unpredictable economic and security situation, as in the past, as well today, migrations have not stopped, continue to happen today, right across the European Union and the countries of the Atlantic, such as Canada, the US and Australia, where there are better conditions for life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".