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Record W2966599465 · doi:10.5539/ies.v12n8p33

Teachers’ Knowledge, Beliefs, and Attitudes about Climate Change

2019· article· en· W2966599465 on OpenAlexvenueno aff
Dominique‐Esther Seroussi, Nathan Rothschild, Eyal Kurzbaum, Yosi Yaffe, Tahel Hemo

Bibliographic record

VenueInternational Education Studies · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeKnowledge levelPolitical economy of climate changePsychologySample (material)Action (physics)Environmental resource managementEnvironmental scienceMathematics educationEcology

Abstract

fetched live from OpenAlex

A sample of eighty Israeli in-service teachers filled out a questionnaire assessing their beliefs about the existence and the anthropogenic origin of climate change, their knowledge about the causes and consequences of climate change and the actions which can be taken to remediate it, as well as their level of concern about it and their readiness to act and teach in a climate-friendly way. The results show gaps in knowledge especially regarding the consequences of climate change, and misconceptions about the causes of climate change. The anthropogenic nature of climate change is well admitted. The percentage of teachers ready to take action to slow down climate change is smaller than the percentage of teachers understanding climate change. There is a significant correlation between knowledge about the consequences of climate change and concern about it and readiness to act. These results lay out the path for possible improvement of climate change instruction in teachers’ preparation programs.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.370
Teacher spread0.338 · 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 designObservational
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

Citations50
Published2019
Admission routes1
Has abstractyes

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