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Record W2810332281 · doi:10.2495/dne-v13-n2-147-155

A methodology for illegal settlements re-conversion

2018· article· en· W2810332281 on OpenAlexvenueno aff
Donatella Cialdea, Nicola Quercio

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Planning and Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsHuman settlementSettlement (finance)UrbanizationBusinessEnvironmental planningOrder (exchange)Residential areaGeographyPolitical scienceEconomic growthLawEconomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.488
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.330
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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