Person-first language: does it matter when describing persons who sexually offended?
Bibliographic record
Abstract
Negative community attitudes towards persons who have sexually offended may be detrimental to community reintegration. Poor community reintegration is a problem as it is linked to various factors that increase the likelihood that a released person convicted of a sexual offense will commit another crime in the future. Past literature has found that the ‘sex offender’ label serves to exacerbate negative perceptions through perpetuating stereotypes that include ‘all persons who sexually offend are dangerous and incurable.’ Person-first language has begun to replace labels as a means to put the person before the behaviour and lessen the immediate negative response. The aim of this study was to test whether person-first language could result in less negative perceptions made about a fictitious person being released into the community following a conviction for sexual offending. Two hundred and ninety one Canadian participants read one of eight randomly assigned public announcement vignettes and then proceeded to answer questions regarding their perceptions of persons who sexually offend. The results indicate that the Canadian participants continued to endorse negative perceptions of the population irrespective of the label used, suggesting that the labels were not perceived differently. However, when a person-first label was compared to ‘rapist,’ and ‘pedophile,’ participants reported less negative perceptions pertaining to treatment amenability. Implications for how information is disseminated by the media to the public will be discussed.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".