Bibliographic record
Abstract
This chapter provides an introduction to the field of clinical forensic psychology. It focuses on four general topics. First, we provide a definition of forensic psychology and a discussion of how it fits within clinical psychology, arguing that psychologists who work in the field of forensic psychology must have specialized training and experience in the field. Second, we discuss the legal parameters within which forensic assessments are conducted and note that legal standards establish the parameters of the assessment and help focus the clinician's task. We introduce and discuss the psycholegal content analysis approach to forensic assessment. Next, we review some of the contemporary issues in forensic assessment, including the effect of the clinical versus actuarial debate for the field, the development of the legally informed practitioner model, the roles and limits of general psychological testing in forensic contexts, legal specificity and training in forensic psychology. Finally, we discuss some future concerns that should be addressed in the field. In particular, we raise concerns about quality control in forensic assessment and identify areas that require further development (i.e., civil forensic assessment, forensic assessments with youth, women, and visible minorities).
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.025 |
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".