Shedding light on public perceptions of scientists who engage in wrongness admission amidst a failed replication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it