François-Emmanuël Boucher. 2020. Le Trumpisme. Contribution à l’analyse rhétorique du discours national-populiste (Québec : P. U. Laval).
Bibliographic record
Abstract
Le travail significativement intitulé « Le Trumpisme » (2020) de François-Emmanuël Boucher, professeur au Département de Langue française, Littérature et Culture du Collège militaire royal du Canada, s'inscrit dans et, en même temps, contribue à plusieurs domaines et vecteurs de la pensée scientifique contemporaine.D'une part, on peut le considérer comme faisant partie d'une tendance importante des dernières décennies qui conceptualise le phénomène du populisme.D'autre part, l'ouvrage offre une analyse argumentative extensive d'un cas de figure précis : le phénomène (politique et discursif) que représente le 45e président américain, Donald Trump.De surcroît, en poursuivant cet objectif global, l'auteur entreprend une analyse sociohistorique et contextuelle du phénomène « Trump » afin de dresser un bilan (décevant) de l'évolution future des modèles politiques et de la société dans son ensemble.François-Emmanuël Boucher.2020.Le Trumpisme.Contribution à l'analyse rhéto...
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".