Hostilité et ambivalence entre la langue et la norme dans le discours normatif des remarqueurs et chroniqueurs de langue
Bibliographic record
Abstract
Normative discourse plays a predominant role in discourse relating to language and its use. This applies to pre-scientific, non-scientific and scientific discourse. The relationship between “language” and “norm” can generally be described as ambivalent in these discourses. But could it be conceived and staged in discursive construction as a relationship imbued with enmity? This is the general question that will guide our analysis based on a variety of documents, including remarques on the French language in the 17th and 18th centuries and chronicles of language and critiques of French in the 20th century. We will also ask to what extent any hostile attributions and representations are ideologically motivated and are therefore an expression of the relationship between language practices and social positions.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.057 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".