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Record W3176325680

A Historical Review and Resource Guide to the Scholarship of Teaching and Training in Psychology and Law and Forensic Psychology

2015· review· en· W3176325680 on OpenAlexaboutno aff
Cynthia J. Najdowski, Bette L. Bottoms, Margaret C. Stevenson, Jennifer C. Veilleux

Bibliographic record

VenueScholars Archive - University at Albany (University at Albany, State University of New York) · 2015
Typereview
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipForensic psychologyField (mathematics)Resource (disambiguation)Training (meteorology)PsychologyLegal psychologyPsychological researchEngineering ethicsPolitical sciencePedagogyLawSocial psychologyComputer scienceEngineeringCriminology
DOInot available

Abstract

fetched live from OpenAlex

The field of psychology and law, including forensic psychology, is an exciting concentration of research activity and student training and has grown rapidly, but to what extent have teaching and training efforts in the field been systematically catalogued and evaluated? We conducted a historical review and content analysis of the American and Canadian literature on the scholarship of teaching and training in the field. This review catalogs (a) information related to the development of training and teaching, (b) descriptions of programs and courses at various levels, and (c) articles on teaching resources or techniques in this field. We hope it serves as a useful guide to the literature resources for those in the field and a catalyst for future interest in this area of scholarship, especially for future evaluations of teaching and training.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0260.038
Science and technology studies0.0020.002
Scholarly communication0.0030.005
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.009

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.

Opus teacher head0.078
GPT teacher head0.338
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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