Empirical Evidence on Agricultural Land-Use Change in Sardinia, Italy, from GIS-Based Analysis and a Tobit Model
Bibliographic record
Abstract
An important part of the Sardinian Regional Operational Programme (ROP) 2000–2006 is represented by the policies funded by the European Agricultural Guidance and Guarantee Fund (EAGGF), aimed at maintaining agricultural land uses and improving the quality of agricultural land. Such investments, spread over almost all Sardinian cities, attempted to support local development based on the traditional primary sector of production. This article analyses the investment policies implemented by the Sardinian Region through the 2000–2006 EAGGF-based part of the ROP (2000–2006 ROP-EAGGF), in order to assess their effectiveness. This assessment of effectiveness, implemented in the context of other signals concerning local development such as income and urbanization, is very important to address the ongoing policies of the 2007–2013 Rural Development Programme and the question of geographic concentration of investments. The article analyses the results of the 2000–2006 ROP-EAGGF through a geographic information system, by means of a Tobit model, and proposes an analytical and interpretive approach that can be easily exported to other public planning processes in order to implement investment in agriculture.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".