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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. (PsycINFO Database Record (c) 2017 APA, all rights reserved)

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.685
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 teacher head, not a consensus.

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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