La reception du roman au Quebec et au Canada anglais dans les annees mille neuf cent cinquante
Bibliographic record
Abstract
What is a classic ? To this day, no consensus exists on the definition of the literary classic , or on the definition of canon. It is an open debate, to which this master thesis participates. For many centuries, classics were selected for various aesthetic criteria that seemed to go 'without saying', and that scholars did not feel the need to justify. During the second half of the twentieth century however, views on the concept began to change. Debates emerged on the universality of its definition, and the idea that subjectivity might govern its selection process started to be examined. The literary Institution, as presented by Jacques Dubois, became for most critics the main agent of canonization, and aesthetic was demoted to a factor amongst others. Still, whether we are discussing canonic genre or ideology, we are forced to admit that the Institution does not discriminate its classics the same way everywhere. Of all those elements of subjectivity now taken into consideration in the field of canonical studies, it is the cultural aspect of the process that interests us here, and more precisely its manifestation in French and English Canada. By examining the critical reception of six Canadian classics published in the 1950's, we hope to find how those novels gained their recognition, and therefore shed some light on the Canadian way of envisioning the literary canon.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".