Gender, emotionality, and victim impact statements
Bibliographic record
Abstract
Purpose The present study aims to examine the influence of emotional content and gender pertaining to victim impact statements (VIS) on sentencing outcomes. Design/methodology/approach The authors used a 2 (emotionality)×2 (participant gender)×2 (victim gender)×2 (statement gender) factorial design. Participants (n=715) read a crime vignette and corresponding VIS, and completed questionnaires pertaining to sentencing recommendations, legal attitudes, and levels of emotional empathy (counterbalanced). Findings Results indicated that participant gender was related to the emotional appeal of the VIS, and ratings of punishment severity. Emotional empathy was positively associated with perceptions of credibility and emotionality. Higher legal attitudes scores were positively correlated with higher minimum sentences, ratings of credibility, emotional appeal, as well as more severe punishments. Originality/value This study has important implications with respect to perceptions of VIS in relation to how emotional they are, who the victim is, who the statement is written by, and who hears the statement. Given the lack of previous research in this area, the study provides data that warrant further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".