Analysis of agricultural land use transformations in Greece: a multinomial logistic regression model at the regional level
Bibliographic record
Abstract
In the past few decades, numerous structural changes regarding the socio-economic basis of most EU countries have been profound and critical.These processes of economic restructuring have resulted in significant land use changes.As regards the agricultural sector, the overall changes in both Greece and the other European countries have been particularly intense in the last 20 years.Such changes include the massive reduction in the levels of employment in agriculture, shrinkage of the economic importance of the agricultural sector as a whole, changes in crop plants and cultivation practices, crucial implications arising from the new European Common Agricultural Policy and the growing competition due to low-cost agricultural products from developing countries.These changes have not had the same magnitude and impacts across all Greek regions.Instead, significant spatial variability relevant to the regional characteristics of each administrative prefecture can be observed.In this article, we carry out an empirical analysis focusing on agricultural land use patterns at a prefectural level for the whole country.The changes are tracked and analysed in terms of selective possible driving factors.The methodology adopted is multinomial logistic regression.Some policy implications are drawn with a regional perspective.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".