Bibliographic record
Abstract
Since its settlement in New France, the French-speaking population of Canada has maintained a close but ambiguous relationship with other, English-speaking population groups on the American continent. These relations have been characterized not only by feelings of intense attraction, especially among the working class and lower classes of society, but also by a certain distrust that may be gleaned in the discourse of the elite, as the work of historians Gérard Bouchard (2000), Yvan Lamonde (2001), and Paul-André Linteau (2000) show. Their studies reveal the similarities between Québécois and other New World communities on the continent, as well as the numerous links that connect the Québécois and American cultures. In the same way, the close resemblance between the Québécois literary imagination and its American counterpart has been explored in research on the myth of America (Morency 1994) and on the “intérieurs du Nouveau Monde” (interiors of the New World). 1 Despite the clear profile we have of these tendencies, there remains, however, a lack of detailed analysis of the ways in which Québécois writers have become aware of and in some cases familiar with American literature and have drawn inspiration from it. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".