In the shadow of mortality: The impact of priming specific types of death on the terror management literature
Bibliographic record
Abstract
Terror management research manipulates mortality salience (MS) by asking participants to respond to two prompts about death, i.e., briefly describe the emotions that the thought of dying arouses in you, and briefly describe as specifically as you can what you believe will happen to you physically as you die and after you are dead. The fact that death is primed in a uniform manner across all studies leaves a large gap in the literature, i.e., the manner in which death is imagined could impact responses. This likely explains why this theory contains two competing hypotheses to explain the attitude changes that occurs after death reminders. The worldview polarization hypothesis, asserts that individuals will become more polarized in their previous political orientation (Pyszczynski, 2013). On the other hand, the conservative shift hypothesis, claims that people will become more conservative.\n\nThe purpose of the current research is to investigate the origin of these hypotheses by manipulating how death is primed, i.e., by priming specific ways of dying.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".