Controlling the narrative: Euphemistic language affects judgments of actions while avoiding perceptions of dishonesty
Bibliographic record
Abstract
The present work (N = 1906 U.S. residents) investigates the extent to which peoples' evaluations of actions can be biased by the strategic use of euphemistic (agreeable) and dysphemistic (disagreeable) terms. We find that participants' evaluations of actions are made more favorable by replacing a disagreeable term (e.g., torture) with a semantically related agreeable term (e.g., enhanced interrogation) in an act's description. Notably, the influence of agreeable and disagreeable terms was reduced (but not eliminated) when making actions less ambiguous by providing participants with a detailed description of each action. Despite their influence, participants judged both agreeable and disagreeable action descriptions as largely truthful and distinct from lies, and judged agents using such descriptions as more trustworthy and moral than liars. Overall, the results of the current study suggest that a strategic speaker can, through the careful use of language, sway the opinions of others in a preferred direction while avoiding many of the reputational costs associated with less subtle forms of linguistic manipulation (e.g., lying). Like the much-studied phenomenon of "fake news," manipulative language can serve as a tool for misleading the public, doing so not with falsehoods but rather the strategic use of language.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".