Comparative Analysis of Agriculture Policies for Tobacco Planting and Processing, and the Correlation With the Illicit Production and Trade of Tobacco Products in Countries of the Western Balkans
Bibliographic record
Abstract
Tobacco planting and processing has a long tradition in the Western Balkan region, including Albania, Kosovo, Montenegro and North Macedonia; over the last 20 years, however, farmers have faced a significant decrease in production. In Montenegro and Kosovo, for example, the surfaces planted with tobacco are almost inconsequential.Agricultural policies and legal and procedural frameworks regulate all related processes, such as tobacco seed distribution, registration of farmers, disclosure of land farmed for tobacco, production yield, and the various collecting and processing stages, as well as the enforcement capacities of the related law enforcement institutions. These factors have significantly impacted tobacco production and trade, including the levels of illicit production and trade.This paper is based on empirical analysis, evaluation of the statistical data of tobacco-related state policies and country interviews related to tobacco production costs in the region, which affect sector-related policies.Our findings indicate that Albania has a lack of clear and coordinated policies, procedures, and enforcement capacities to regulate and monitor all processes, from planting to the processing and trade of tobacco. North Macedonia is in a much better situation in this regard and a new draft tobacco law, associated with a series of implementation regulations is expected to result in further improvements. In the Albanian case, strong evidence suggests that there are considerable tracts of land planted with tobacco and many illegal tobacco-processing plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".