Shedding light on public perceptions of scientists who engage in wrongness admission amidst a failed replication
Bibliographic record
Abstract
Admitting that one’s research findings are wrong involves admitting a potential instance of incompetence, which can keep scientists from engaging in wrongness admission. However, wrongness admission can yield favorable perceptions. In five experiments ( N = 2420), we tested whether wrongness admission yields higher perceived trustworthiness in the scientist and trust in science and discipline-specific research as well as public funding support for the scientist, science, and discipline-specific research. Scientists engaging in wrongness admission (vs refuse or do not comment) were perceived as more trustworthy and received more support for federal funding for their own research. Moreover, wrongness admission yielded similar levels of science and discipline-specific public funding support. Wrongness admission not only facilitated higher scientist trustworthiness, but trustworthiness was, in turn, associated with greater trust in science and psychology, as well as scientist and psychology public funding support. This work highlights potential benefits of scientist wrongness admission amidst failed replications.
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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 | 0.136 | 0.387 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".