The transition to scientific research and the fallout of speaking publicly: perspectives from a former proponent of “body language” pseudoscience
Bibliographic record
Abstract
Despite decades of research and thousands of peer-reviewed articles on nonverbal communication written by a worldwide community of academics, a number of people in position of power, including security, justice and legal practitioners have embraced “body language” pseudoscience. This autoethnography aims to offer an otherwise inaccessible glimpse of the process a person can go through when turning to and away from pseudoscience. To achieve this objective, I describe and reflect upon the main events that, as a young lawyer, influenced my transition from body language pseudoscience to scientific research. To shed additional lights on these events, I turn to the cyberbullying and intimidation attempts that followed my journey and my decision to speak publicly. This autoethnography ends with a call for scholarly institutions to adequately protect researchers, including graduate students, from cyberbullying and intimidation attempts.
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 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 | 0.048 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.037 | 0.079 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.010 | 0.037 |
| 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".