Marie-Claire Blais and Dostoevsky: Observations from the Notebooks
Bibliographic record
Abstract
This article demonstrates the pervasive presence of Dostoevsky in the oeuvre of Canadian writer Marie-Claire Blais—three-time winner of the Governor-General’s Award for literature—through analysis of her working notebooks and individual novels. Blais’ thirteen working notebooks (1962–1974) contain one hundred eighty references to works and characters in Russian literature, some sixty of which relate to Dostoevsky. Analysis of these references shows that Blais throughout her formative years studied Dostoevsky rigorously and thoroughly. Using the seven mentions of Alyosha from The Brothers Karamazov as a starting point, we examine Blais’ creation of a series of Alyosha-figures in Un Joualonais sa Joualonie (1973), Visions d’Anna, ou le vertige (1982), and Dans la foudre et la lumière (2001), each representing a possible alternative development of the character-type. The theme of innocent suffering (Ivan Karamazov’s “the single tear of a child”) is traced in David Sterne (1967), Un Joualonais sa Joualonie (1973), and Soifs (1995).
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.032 | 0.016 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".