Afforestation of rural land in greece: a multinomial logistic regression analysis of driving factors
Bibliographic record
Abstract
This article deals with the importance of European Agricultural Fund for Rural Development through the implementation of afforestation schemes in rural communities.The main aim of the article is to investigate the spatial patterns of afforestation in Greece, the driving factors behind these patterns as well as the degree of the success of the EU policy for forest expansion through afforestation of arable land.Therefore, the focus is on providing a concrete appraisal regarding the contribution of EU 2080/92 and 1257/99 Regulations to the improvement of regional forest status by means of increasing forest areas and improving the local people's standard of living.The study area covers the entire Greek territory which consists of 51 administrative prefectures.Methodologically speaking, the empirical analysis is based on a multinomial logistic regression model targeted at providing a thorough understanding of the major driving factors that influence rural communities' response to the regulations.The environmental importance of arable land afforestation is highlighted as well as the extent to which the regulations has met the initial expectations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".