M. Dargnat – Linguistic Profiles in Social and Fictional Settings – 2007 MLA Convention Linguistic Profiles in Social and Fictional Settings
Bibliographic record
Abstract
Background Shaping the linguistic identity of characters in novels or in drama is a well-known literary practice. Characters are associated with linguistic features such as syntactic, lexical and pronunciation peculiarities. In this talk, I describe two different functions of linguistic profiles, focusing on five plays by the Québécois writer Michel Tremblay, which span a period of thirty years (1968-1998). I show that the linguistic markers initially help Tremblay construct a socio-linguistic array that he connects to the fictional structure in his last two plays. Method In order to construct a standard of comparison, I extracted from linguistic descriptions of colloquial quebecois French (henceforth CQF) 12 variables, including for instance the occurrence of prepositions with bare nouns (à matin lit. ‘in morning’, standard: au matin or le matin) or of the interrogative affix tu (ils viennent-tu? lit. ‘they come-INT’, standard: ils viennent?). Summing up the scores for these variables, I investigated the frequency distribution across characters in the different plays. Results Non-parametric statistical tests show that there is in general a strong correlation between linguistic features and the social origin of characters. For instance, in Les Belles-sœurs and L’Impromptu d’Outremont, the social context of characters is reflected by the distinctive prominence/absence of popular features. More interestingly, in Le Vrai Monde and Encore Une Fois, Si Vous Permettez, popular language indicators point to different temporal or narrative manifestations of the same individual. E.g., in Encore Une Fois, the oldest character (called Le Narrateur) speaks a markedly formal/literary variety of French whereas the very same entity, when younger, speaks a more popular variety. In addition, the older Narrateur speaks to the audience, thus creating a metaleptic effect (Genette), whereas its younger versions remain ordinary characters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".