Bibliographic record
Abstract
Over the past twenty-five years, education and certification in forensic psychiatry have grown more uniform and systematic. In 1982, the American Academy of Forensic Sciences (AAFS) and the American Academy of Psychiatry and the Law (AAPL) cosponsored a report, entitled Standards for Fellowship Programs in Forensic Psychiatry (AAFS-AAPL Joint Committee 1982). That report fostered a common didactic and experiential core in training programs in the United States and Canada. The creation, in 1988, of the Accreditation Council on Fellowships in Forensic Psychiatry (ACFFP), a semi-autonomous component of AAPL, furthered that end by creating a process to distinguish training programs that met the Standards for Fellowship Programs in Forensic Psychiatry from training programs that did not meet the Standards. The ACFFP accredited fellowships from 1989 until 1997. Midway through 1997, the ACFFP was supplanted by the Accreditation Council for Graduate Medical Education (ACGME). There has been a corresponding change in nomenclature, i.e., the ACFFP referred to forensic training programs as fellowships, whereas the ACGME refers to forensic training programs as residencies. As of July, 2002, the ACGME had approved thirty-eight forensic residency programs (with a total of ninety-two trainee positions) as meeting its criteria for accreditation (ACGME 2002).
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.019 |
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".