Bibliographic record
Abstract
Welcome to our first edition of the Journal of the Academy of Forensic Nursing (JAFN).It has been a little while coming-many new discoveries as the idea grew, a long wait and much labour!We are excited to begin this journey to provide you with a source of evidence and information for your forensic nursing practice.The Academy of Forensic Nursing (AFN) is committed to excellence in evidence-based, trauma-informed forensic nursing practice.Forensic nursing is rapidly expanding globally, unfortunately in response to growing violence, disasters and increasing recognition of the impact of violence across the lifespan.Nurses bring an understanding of the impact of trauma and medicolegal considerations into their comprehensive practice.This requires that the forensic nurse is continually learning.As such, we have developed this peer-reviewed journal to address numerous aspects of practice.Our new journal adds to the tools that a forensic nurse can use to remain current.The journal is "open source", meaning that anyone can access these articles.This is important for supporting evidence based practice.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.083 | 0.059 |
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".