Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".