A methodology for illegal settlements re-conversion
Bibliographic record
Abstract
This paper describes the phenomenon of illegal settlements.The case study focuses on the Molise region in the South of Italy, which is characterized by the absence of wide-area plans.In fact, the weakness of the existing plans at municipal level meant that the increase in buildings could not be controlled, especially in non-urban areas.This resulted in urban areas, planned for the supply of services, remaining agricultural zones and agricultural areas becoming useful for new residential expansions.Our dissertation explores the effects of Molise Regional Law no.17/1985, following the enactment of National Law no.47/1985 (rules for building planning control).The national law imposed on municipalities the task of cordoning off illegal settlement areas in order to reclassify them into new planning tools.The problem was that the new classification, oriented to residential utilization, requires the realization of appropriate facilities (urbanization, public parks, schools, parking areas, etc.) that do not actually exist.Consequently, in many cases it was necessary to draft appropriate renewal plans to include these facilities.In fact, the national law was oriented to support a special type of planning tool, delegating detailed rules to regions.This issue was analyzed by the l.a.co.s.t.a.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".