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Record W2167622108

People's Ideas about Climate Change: A Source of Inspiration for the Creation of Educational Programs

2001· article· en· W2167622108 on OpenAlexaff
Diane Pruneau, Linda Liboiron, Emilie Vrain, Hélène Gravel, Wendy Bourque, Joanne Langis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsPhenomenonEnvironmental educationHumanitiesPsychologyWorryFeelingClimate changeSocial psychologySociologyPedagogyPhilosophyEcologyEpistemology
DOInot available

Abstract

fetched live from OpenAlex

The global nature of the phenomenon, the complexity of climatic knowledge, and the difficulty of modifying human behaviour complicate the choice of efficient strategies in climate change education. A qualitative study conducted with children, teenagers, and adults allowed researchers to discover people’s ideas (knowledge, opinions, feelings) about the phenomenon: adults, some teenagers, and few children have heard of climate change. Participants can describe the problem without being able to identify its causes and consequences. Climate change arouses little worry because many participants estimate that the phenomenon will have no tangible consequences on their life. Teenagers are less confident than adults regarding the possible mobilization of the population to decrease their impact on the climate. Finally, educational strategies trickling down from these

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.005
Scholarly communication0.0050.005
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.015
GPT teacher head0.285
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations77
Published2001
Admission routes1
Has abstractyes

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