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Record W1995570069 · doi:10.1080/00958964.2012.759521

Competencies Demonstrated by Municipal Employees During Adaptation to Climate Change: A Pilot Study

2013· article· en· W1995570069 on OpenAlex
Diane Pruneau, Jackie Kerry, Sylvie Blain, Evgueni Evichnevetski, Paul Deguire, Pierre‐Yves Barbier, Viktor Freiman, Jimmy Therrien, Joanne Langis, Mathieu Lang

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Journal of Environmental Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsAdaptation (eye)Vulnerability (computing)Futures contractClimate change adaptationProcess (computing)Climate changeEnvironmental educationPsychologyEnvironmental resource managementEnvironmental planningKnowledge managementBusinessPedagogyGeographyComputer scienceEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Abstract Since coastal communities are already subjected to the impacts of climate change, adaptation has become a necessity. This article presents competencies demonstrated by Canadian municipal employees during an adaptation process to sea level rise. To adapt, the participants demonstrated the following competencies: problem solving (highlighting components of the problem and identifying constraints), futures thinking, risk prediction, vulnerability analysis, local knowledge, planning, and communication. However, some competencies that could be potentially useful in adaptation were used less frequently by participants: developing solutions, knowledge of adaptation, math skills, hope, and self-efficacy. Keywords: adaptationclimate changecompetenciesenvironmental education

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.288
Teacher spread0.255 · 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