Managing the terror of publication bias: A comprehensive p-curve analysis of the Terror Management Theory literature
Bibliographic record
Abstract
Abstract We assessed the evidential value of the large literature (k=826 studies) investigating the mortality salience (MS) hypothesis from terror management theory. We employed a multitool assessment approach and reviewed past efforts to replicate experiments testing the MS hypothesis, and conducted a p-curve, a z-curve, and a random effects meta-analysis including bias corrections of the selection model, PET-PEESE, and WAAP-WLS on the studies. Overall, the different meta-analytic tools pointed to conflicting conclusions, reflecting differences in the methodology and philosophy of these tools. Our synthesis of these findings suggests there are true effects underlying some studies of the MS hypothesis, although the effects are highly heterogeneous, and the majority of studies are underpowered. We recommend future replications to assume a smaller effect size (r = .10 ~ .15) and to follow expert guidance in the experimental protocol. Given the conflicting findings that emerged, we suggest that future attempts to evaluate other literatures would benefit from a multitool assessment approach.
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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 | 0.474 | 0.774 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.037 |
| Bibliometrics | 0.048 | 0.036 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".