Nonverbal communication and deception detection: A short conversation between three young researchers
Bibliographic record
Abstract
This conversation was published in French in the journal Commposite, and is translated herein with the authorization of this journal. Reference : Denault, V., Delmas, H. et Zloteanu, M. (2021). Communication non verbale et détection du mensonge : Une conversation entre trois jeunes chercheurs [Nonverbal communication and deception detection: A short conversation between three young researchers]. Commposite, 22(1), 107-119, Nonverbal communication and deception detection: A short conversation between three young researchers. In this conversation led by Vincent Denault of McGill University (Montreal) with Hugues Delmas of the École Pratique des Hautes Études (Paris) and Mircea Zloteanu of Kingston University (London), the three young researchers working on nonverbal communication and deception detection share their thoughts on the limits of research on these topics, as well as on the difficulties associated with the dialogue between researchers and practitioners. (20) (PDF) Nonverbal communication and deception detection: A short conversation between three young researchers.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.000 |
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.
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 teacher head, 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".