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Record W3033027482 · doi:10.46303/jcsr.02.01.8

Teaching the Climate Crisis: Existential Considerations

2020· article· en· W3033027482 on OpenAlexaff
Cathryn van Kessel

Bibliographic record

VenueJournal of Curriculum Studies Research · 2020
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsExistentialismTerror management theoryMortality salienceSalience (neuroscience)Death anxietyPsychologyAnxietyEpistemologySociologySocial psychologyEnvironmental ethicsPolitical scienceCognitive psychologyPhilosophy

Abstract

fetched live from OpenAlex

It is urgent that educators in social studies and science (among other disciplines) consider the ethical imperative of teaching the climate crisis—the future is at stake. This article considers a barrier to teaching this contentious topic effectively: existential threat. Through the lens of terror management theory, it becomes clear that climate catastrophe is an understandably fraught topic as it can serve as a reminder of death in two ways. As will be explained in this article, simultaneously such discussions can elicit not only mortality salience from considering the necrocene produced by climate catastrophe, but also existential anxiety arising from worldview threat. This threat can occur when Western assumptions are called into question as well as when there is disagreement between those with any worldviews that differ. After summarizing relevant aspects of terror management theory and analyzing the teaching of the climate crisis as an existential affair, specific strategies to help manage this situation (in and out of the classroom) are explored: providing conceptual tools, narrating cascading emotions, carefully using humor to diffuse anxiety, employing language and phrasing that does not overgeneralize divergent groups, and priming ideas of tolerance and even nurturance of difference.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 integrity0.0000.001
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 head0.199
GPT teacher head0.499
Teacher spread0.300 · 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.

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

Citations38
Published2020
Admission routes1
Has abstractyes

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