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Record W4311460642 · doi:10.1016/j.cacint.2022.100097

Climate change adaptation cycle for pilot projects development in small municipalities: The northwestern Italian regions case study

2022· article· en· W4311460642 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCity and Environment Interactions · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
FundersSt. Paul's FoundationCompagnia di San Paolo
KeywordsMaladaptationAdaptation (eye)Climate changeEnvironmental resource managementContext (archaeology)Environmental planningPsychological resilienceLocal adaptationClimate change adaptationPopulationBusinessGeographyEnvironmental science

Abstract

fetched live from OpenAlex

More than half of the European population live in small and medium size municipalities, where climate adaptation planning is an under-researched topic within the climate change field. Many constraints might hinder the implementation of adaptation pilot projects due to lack of economic, knowledge, and technical available resources. Local institutions find difficulties in building a coherent local adaptation planning and design processes with international and national frameworks. In this context, this article proposes a methodology based on the available international frameworks to support the small communities with the aim to implement adaptation pilot projects within different sectors. In doing so, this paper tests a climate change adaptation cycle for pilot projects development in small municipalities; the first in Italy for small municipalities under 20.000 inhabitants. The proposed methodology could lead local adaptation initiatives in climate change risk assessment by supporting the research communities in developing a coherent vision for the local territories and to identify proper oriented measures to enhance demonstrative pilot projects and to increase the level of resilience in small municipalities, avoiding maladaptation.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.999

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 studies0.0020.000
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 head0.363
GPT teacher head0.344
Teacher spread0.020 · 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