2565 – Global Attitudes Towards Forensic Psychiatry (2006–2012)
Bibliographic record
Abstract
Introduction: Forensic psychiatry is a branch of psychiatry which emphases on the interface between law and psychiatry. It may include psychiatric consultation in a wide variety of legal matters, as well as clinical work with perpetrators and victims.The objective of current study is to visualize the scientific activities in the field of Forensic psychiatry through 2006–2012. Methods: The Science Citation Index Expanded (SCI-E) and Social Science Citation Index from the database of Web of Science were used to obtain all publication entitled as“Forensic psychiatry” through 2006–2012. Restriction of publication entitled into Forensic psychiatry cause to obtain the most relevant papers in the field of Forensic psychiatry. Results and conclusion: Analysis of data showed that a total number of 248 papers entitled as Forensic Psychiatry were published in the journals which indexed in Web of Science during the period of study. The most majority of publication came from North America and west Europe. USA sharing 23% of global publication in the field is the most productive country followed by Germany (19%), England (14%), and Canada (7%). More than 63% of global publication came from these four prolific countries. A total number of 271 organization have contributed their works in the field of Forensic Psychiatry, the American private University of Yale was the most productive among them.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".