La locution entre nous : un marqueur d’attitude polémique
Bibliographic record
Abstract
Notre but dans cet article est de faire une analyse de la locution entre nous en tant qu’opérateur sémantico-pragmatique. En nous situant dans le cadre de la polyphonie énonciative – cf. Anscombre, Donaire & Haillet 2013 –, il s’agira de voir dans quelle mesure la présence de entre nous dans un énoncé particulier détermine l’agencement des différents points de vue convoqués lors de l’apparition de cet énoncé. Notre intérêt est de montrer que la locution entre nous renvoie à un point de vue précédent auquel elle dit s’opposer et ouvre ainsi un cadre polémique où insérer les contenus du message communiqué. Afin de justifier cette hypothèse, nous proposons une série de critères, tant distributionnels que sémantico-pragmatiques, qui montreront le type de stratégie discursive mise en place par l’emploi de entre nous.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".