Bibliographic record
Abstract
The stylistic analysis of literary texts from Québec has been a relatively neglected field of research, although Québec writers themselves have always had a marked interest in investigating the language of writing and this attitude has had an impact on literary form. But the means of expression must be examined if the literature is to be fully understood. Mapping the special vocabulary and describing its development through literary works would undoubtedly contribute to an understanding of Québec literary stylistics. Other chapters might deal with the technique of inserting homonyms (i.e. identical signifiers in Québec French and standard French) in literary texts, with the tendency toward the synonymization of words that have the same meaning but express it with different signifiers and, finally, with the role of free indirect discourse in the introduction of "Quebecisms" into the literary language.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".