Instantaneous Errors and Body Gestures in Some American Political Contexts
Bibliographic record
Abstract
The purpose of this research study is to give a psycholinguistic investigation of some of Trump's smiles, zone distances, handshaking styles, and speech errors. It is carried out under the framework of contextual congruence, which states that what speakers say, and their body movements should be compatible. To create a personal sketch for Trump, 73 short movies and 119 pictures were meticulously scrutinized. 81.4% of images and more than 65.3% of videos indicated phony grins provided to specific people, including Michael Flynn, Mike Pence, and John Kelly. Trump's genuine smile was only noticed when he was with one of his family members, particularly his daughter. With the exception of Nancy Pelosi, the results also suggested that Trump tended to breach his political foes’ intimate zones as a form of nonverbal political counterattack. The results also revealed that Trump's recursive handshake style was a double-handed handshake. He employed this method to demonstrate the sincerity of affection toward the recipient. He regularly gave this handshaking approach to various Asian politicians, who felt irritated by it, since Trump used to perform this handshaking in 30 seconds. Donald used all of his body language and facial clues to control not only his adversaries but also his allies and his speech errors reflected the same facts.
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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".