Quelle est la spécificité des discours électoraux? Le cas de Stephen Harper
Bibliographic record
Abstract
Résumé. Cette étude de cas démontre que le discours électoral possède des caractéristiques propres. On donne l'exemple de Stephen Harper dont les discours tenus lors des élections de 2008 se différencient de ceux qu'il a prononcés à titre de chef de gouvernement. Le discours électoral est plus ancré socialement. C'est aussi un discours qui valorise le collectif national; le locuteur privilégie l'emploi du «nous», plutôt que de se présenter comme principal responsable des choix collectifs. Comparativement aux discours gouvernementaux, le discours électoral est aussi moins abstrait et plus orienté vers l'action, comme l'indique la prédominance du groupe verbal sur le groupe nominal. La forte présence de la construction négative et la désignation des adversaires (noms propres) soulignent le caractère polémique du discours électoral. Abstract. This case study demonstrates that electoral speeches possess specific characteristics. We give the example of Stephen Harper's speeches, given during the 2008 elections, which differ from those delivered when he was prime minister. The electoral speech is more socially anchored. It values the nation. When he is campaigning, S. Harper also uses the pronoun “we” more frequently, so that he does not appear as the main decider of collective choices. Compared with governmental speeches, electoral speeches are also less theoretical and more action orientated as indicated by the predominance of verbal over nominal groups. The very frequent presence of the negative construction and the use of opponents' names highlight the polemic character of electoral speeches.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".