Bibliographic record
Abstract
Donald Trump's speeches and messages are characterized by terms that are commonly referred to as "thick" or "emotive", meaning that they are characterized by a tendency to be used to generate emotive reactions.This paper investigates how emotive meaning is related to emotions, and how it is generated or manipulated.Emotive meaning is analyzed as an evaluative conclusion that results from inferences triggered by the use of a term, which can be represented and assessed using argumentation schemes.The evaluative inferences are regarded as part of the connotation of emotive words, which can be modified and stabilized by means of recontextualizations.The manipulative risks underlying the misuse and the redefinition of emotive words are accounted for in terms of presuppositions and implicit modifications of the interlocutors' commitments.Rsum: Les discours et les messages de Trump sont caractriss par des termes couramment appels affectif et par une tendance tre utiliss pour gnrer des ractions affectives.Cet article tudie comment la signification affective est lie aux motions et comment elle est produite ou manipule.La signification affective est analyse comme une conclusion valuative rsultant d'infrences dclenches par l'utilisation d'un terme, qui peut tre reprsent et valu l'aide de schmas d'argumentation.Les infrences valuatives sont considres comme faisant partie de la connotation des mots affectifs, qui peuvent tre modifis et stabiliss au moyen de recontextualisations. Les risques manipulatoires sous-jacents l'utilisation incorrecte et la redfinition des mots affectifs sont comptabiliss en termes de prsuppositions et de modifications implicites des engagements des interlocuteurs
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".