Does the label <i>really</i> matter when it comes to judgements of people who have committed sexual offences?
Bibliographic record
Abstract
Negative attitudes to people with sexual convictions are key barriers to effective treatment and reintegration. Recently, there have been moves to reduce stigma by using more person-first language. Although this approach may be effective in adjusting attitudes in the desired (i.e. less punitive) direction, no work has explored how such labelling affects judgements of specific individuals with this offending history. In two well-powered experimental studies (Study 1 N = 522; Study 2 N = 470), we find less negative attitudes when this population is labelled using person-first language in a broad psychometric attitudinal measure. However, there were no notable differences in the levels of expressed negativity toward, and desired social distance from, specific people who have committed sexual offences based on the label used to describe them. We discuss the utility of using person-first language in light of these data, and encourage more nuance when discussing the effects of person-first language use.PRACTICE IMPACT STATEMENT While person-first language has been shown to reduce broad negative attitudes toward people with sexual convictions, this approach may have a limited impact on more targeted judgements of specific individuals. Practitioners and policymakers should consider this nuance when designing stigma reduction strategies, balancing the potential benefits of person-first framing with the complexity of its real-world effects.
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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 | 0.039 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".