Justice at risk! An evaluation of a pseudoscientific analysis of a witness’ nonverbal behavior in the courtroom
Bibliographic record
Abstract
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.
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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 | 0.046 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".