Assessing cross-national differences in police officers' domestic violence attitudes
Bibliographic record
Abstract
Purpose Comparative research provides a mechanism to understand how justice systems throughout the world operate. McPhedran et al. (2017) conducted a comparative examination of police officer attitudes about domestic violence (DV) in the USA and Australia and reported fairly high levels of agreement among male and female officers within each country. The current study builds on these findings by examining officer attitudes toward DV among male and female officers cross-nationally. This was accomplished by examining whether American and Australian male and female officers agree with one another on a number of DV issues. Design/methodology/approach Two-way ANOVA was used to examine the effect of two factors (gender and country) on law enforcement officer attitudes about DV. Findings The results suggest that male and female officers from the USA and Australia significantly differ on 14 of 24 attitudes about DV with the greatest number of attitudinal differences found between American and Australian male officers. Research limitations/implications Scholars who conduct future research examining police officer attitudes about DV should use the instrument from this study as a springboard to develop an updated survey in terms of content and one that would be applicable to cross-national analyses. Methodological study limitations are described in depth in McPhedran et al. (2017). Originality/value While gender differences in attitudes have received scholarly attention, questions remain regarding the degree to which attitudes align among male and female officers across different countries. The current study seeks to fill these gaps in knowledge by examining attitudes about DV between American and Australian law enforcement officers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".