Diversity in the <i>International Journal of Forensic Mental Health</i>
Bibliographic record
Abstract
In this article, we evaluate the extent to which the International Journal of Forensic Mental Health addresses and incorporates discussion of diversity constructs in its publications. Five years of publishing data from 582 manuscript submissions and 164 published articles were reviewed and coded for the inclusion of several diversity constructs (i.e., sex or gender, race or ethnicity, country, age, culture), how these constructs were included in the research (i.e., part of the hypothesis/aims of the study), and the countries the authors and participants represented. Results indicate that most article submissions, authors, and participant samples came from Europe, North America, and Oceania, and these regions had higher acceptance rates. Most articles included studies of clinical populations, and many authors’ primary affiliations were forensic mental health or correctional services. Less than a third of the article titles and over half of the article abstracts mentioned one of the diversity constructs examined. This is somewhat promising and tells us that the journal is publishing articles reporting and examining aspects of diversity in their samples. However, we argue more can be done. Future research and recommendations for the next steps in improving diversity, equity, and inclusiveness in the publication processes and publications are outlined.
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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 | 0.118 | 0.318 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.027 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".