Preparing Pre-Service Educators to Teach Worldview-Threatening Curriculum
Bibliographic record
Abstract
Emotions are central to teaching potentially polarizing content. This study asked pre-service teachers to engage with ideas from Ernest Becker (1973, 1975) and terror management theory (TMT) from experimental social psychology about defensive responses. In group training sessions before their teaching practicum and focus groups after their practicum, pre-service teachers considered the following questions: How might we prevent ourselves, as teachers, from treating a student harshly (or with dismissiveness) when their worldview clashes with ours? What might we need to do with our classes before worldview threatening lessons begin in order to mitigate defensive compensatory reactions? Participants explored how to facilitate contentious and potentially polarizing content and discussions so as to prevent unhelpful, defensive reactions by both students and teachers. This content included derogating people or concepts, coaxing or coercing others to your view, expressing views related to eliminating different worldviews, as well as appropriating aspects of other worldviews. A dramaturgical analysis identified participant objectives, conflicts, tactics, attitudes, emotions and subtexts. Participants explored how to anticipate and avoid worldview threat and self-esteem threat, navigate tense pedagogical spaces, build capacity for expressing uncomfortable emotions and diffuse threat with (appropriate) humour. Through their experiences, these pre-service teachers also increased their own emotional awareness. For participants, TMT became both an attitude and a teachable theory. The authors hope that both direct and indirect uses of TMT in educational contexts can help nourish less fraught social relations, helping us (as educators and humans) gain perspective on our beliefs and those of others without devaluing emotional responses.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".