Applying the Linguistic Imagination Model to Written Corpora: Language Rubrics in the Quebec Press
Bibliographic record
Abstract
This article concerns the Linguistic Imagination ('Imaginaire linguistique') model developed in the 1980s by Anne-Marie Houdebine. It aims to show how this model, designed to increase understanding of the role played by speakers' language attitudes in the assessment of their usage and that of others, can be used to analyze the way discourse on language norms can be constructed. We show how we have used the model to analyze a particular type of normative discourse: ‘language' rubrics, in the form of series of articles devoted to language and published in the press. We show how the Houdebine model allowed us to establish an analytical framework within which we were able to collect and study the various arguments that the columnists use to assess the acceptability of language traits they comment on. We further propose a critical reflection on the model in the light of our analysis, with the particular aim of enriching it. Our analysis concerns a corpus made up of 31 language-rubric articles published in the Quebec press between 1865 and 1996.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".