Taboos and Self-Censorship Among U.S. Psychology Professors
Bibliographic record
Abstract
We identify points of conflict and consensus regarding (a) controversial empirical claims and (b) normative preferences for how controversial scholarship—and scholars—should be treated. In 2021, we conducted qualitative interviews ( n = 41) to generate a quantitative survey ( N = 470) of U.S. psychology professors’ beliefs and values. Professors strongly disagreed on the truth status of 10 candidate taboo conclusions: For each conclusion, some professors reported 100% certainty in its veracity and others 100% certainty in its falsehood. Professors more confident in the truth of the taboo conclusions reported more self-censorship, a pattern that could bias perceived scientific consensus regarding the inaccuracy of controversial conclusions. Almost all professors worried about social sanctions if they were to express their own empirical beliefs. Tenured professors reported as much self-censorship and as much fear of consequences as untenured professors, including fear of getting fired. Most professors opposed suppressing scholarship and punishing peers on the basis of moral concerns about research conclusions and reported contempt for peers who petition to retract papers on moral grounds. Younger, more left-leaning, and female faculty were generally more opposed to controversial scholarship. These results do not resolve empirical or normative disagreements among psychology professors, but they may provide an empirical context for their discussion.
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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 | 0.030 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".