Bibliographic record
Abstract
Albania’s international and internal migrations remain complex phenomena to describe and quantify. Irregular migration adds more complexity to understanding international migration and estimates fluctuate across sources of information. As a result, international and internal migration has collectively changed the demographic landscape of Albania. The consequences go beyond the total population estimate and affect other domains, such as the overall economic and infrastructure changes in the country, the labor market, and gender- and age- ratios. This study attempted to uncover some of the characteristics of contemporary Albanian migration, the paths and trajectories and the rationale behind them, whereas the estimates provided are simply numbers that describe but do not define the phenomenon. The overall objective of the study was to depict the characteristics of international and internal migration in Albania and identify the region’s most affected by these phenomena. Furthermore, the study sought to identify push and pull factors that led to decisions to migrate. The qualitative approach was used to gather information on the internal and international migration of Albanians. This study was based on a two-fold goal. First, to identify and review relevant literature accumulated on the topic of Albanian migration. The study included a strong component of literature review that preceded the data collection phase. When attempting to understand the demographic dimensions of Albania’s internal migration, several conclusions can be reached. The majority of the population is young and this has implications on the labor markets, which are increased in host communities and decreased in communities of origin. The male/female ratio in internal migration is more proportionate that the male/female ratio in international migration. Additionally, coastal and central prefectures are most usually destination regions, whereas north and northeastern prefectures are often source regions. As with internal migration, international migrants are also characterized as young, with more males migrating than females. Furthermore, international migrants tend to work in sectors such as construction, manufacturing, and services, with more women employed in domestic settings. Additionally, international migration is often a multistep process with neighboring countries (Greece, Italy) serving as primary countries of destination and later used as trampolines to migrate to other destinations. The most commonly identified destination countries are Greece, Italy, Austria, Canada, France, Germany, United Kingdom, and the United States. Return migration has emerged as a result of multiple factors, such as difficulties in obtaining proper documentation in destination countries, difficulties in obtaining family reunifications abroad and political and
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".