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Record W2739605467 · doi:10.1080/14789949.2017.1358758

Justice at risk! An evaluation of a pseudoscientific analysis of a witness’ nonverbal behavior in the courtroom

2017· article· en· W2739605467 on OpenAlexaff
Vincent Denault, Louise Jupe

Bibliographic record

VenueJournal of Forensic Psychiatry and Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPseudosciencePsychologyPopularityLie detectionWitnessEconomic JusticeVariety (cybernetics)Nonverbal communicationEmpirical researchSocial psychologyDeceptionEpistemologyDevelopmental psychologyLaw

Abstract

fetched live from OpenAlex

Psychology and law have developed as disciplines through rigorous data collection, exploration and analysis, and the publication of findings through peer-review processes. Such findings are then used to implement evidence-based practices within a variety of settings. However, in parallel to factually and scientifically based knowledge, ‘alternative’ science, or pseudoscience, has gained in popularity. The present case study aims to evaluate the empirical evidence and theoretical underpinnings of a publically accessible analysis of a suspected serial killer’s nonverbal behavior during a bond hearing published online by two ‘synergologists’. The case study emphasizes how a ‘synergological’ analysis to understanding and interpreting human behavior fails to use empirical data, making generalized inferences based on erroneous assumptions. The case study also highlights the detrimental effects such assumptions may have within the justice system and why pseudoscientific analytical approaches should be vigorously challenged by research scientists.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.418
Teacher spread0.342 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations25
Published2017
Admission routes1
Has abstractyes

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