Bibliographic record
Abstract
In this chapter, I examine how a spoken variety of French is used in a corpus of five plays by the Quebecois writer Michel Tremblay. I mainly address two problems. First, I study the way in which general social and literary ideas about language work as filters on the represented linguistic usage. Second, the writer who uses a more or less fictional spoken language in his texts can, in contrast, also transcribe a more standard linguistic usage. Then he can linguistically differentiate between several character types, according to social (i.e. lower vs upper class) or metaliterary criteria (i.e. the position of the speaker in the enunciative stratification of the text). The latter point raises the problem of the linguistic marking of characters’ fictional status. The present study, which is based on selected texts, touches more generally on the issues of literary categorization and textual representation of the linguistic variation and, in this respect, goes beyond the initial corpus. It pertains to the articulation between linguistic analysis and theories of literature, which is crucial for the translation of texts combining several registers.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".