Darwin's illegitimate children: How body language experts undermine Darwin's legacy
Bibliographic record
Abstract
has received and continues to receive much attention from emotion researchers and behavioural scientists. However, the common misconception that Darwin advocated for the universality of emotional reactions has led to a host of unfounded and discredited claims promoted by 'body language experts' on both traditional and social media. These 'experts' receive unparalleled public attention. Thus, rather than being presented with empirically supported findings on non-verbal behaviour, the public is exposed to 'body language analysis' of celebrities, politicians and defendants in criminal trials. In this perspective piece, we address the misinformation surrounding non-verbal behaviour. We also discuss the nature and scope of statements from body language experts, unpacking the claims of the most viewed YouTube video by a body language expert, comparing these claims with actual research findings, and giving specific attention to the implications for the justice system. We explain how body language experts use (and misuse) Darwin's legacy and conclude with a call for researchers to unite their voices and work towards stopping the spread of misinformation about non-verbal behaviour.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".