Re‐Reading Between the Lines: A Re‐Evaluation of the Pragmatic Implications of Minimization Within Police Interrogations
Bibliographic record
Abstract
ABSTRACT Past research has suggested that minimization (i.e., downplaying the moral or psychological seriousness of the crime) pragmatically implies that a suspect will receive a more lenient sentence in exchange for information, and this cannot be mitigated by a leniency warning. Across four studies (Ns = 187, 124, 236, and 241), participants read a case overview involving a break and enter, a suspect‐interview transcript, and then answered questions regarding various perceptions of the interview and potential subsequent judicial process. We manipulated (1) the perspective taken by participants in the follow‐up questions (Self v. Other) and (2) the language and placement of the leniency warning. We then conducted a mini meta‐analysis that incorporated findings from all four studies. Results indicated that minimization only implies leniency when an imagine‐other perspective is used; however, this effect can be successfully negated if a leniency warning is provided directly to the suspect.
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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 | 0.160 | 0.401 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".