Voix des marges, voix en marge : périphéries littéraires et marginalités fictionnelles chez Marie-Claire Blais et Sara Stridsberg
Bibliographic record
Abstract
This dissertation studies two contemporary authors who share a commitment to opening theirfictional works to figures on the margins of the social world, while themselves writing fromterritories situated on the periphery of the international literary space. By exploring themarginalities at these different levels in the works of Marie-Claire Blais and Sara Stridsberg, thisstudy aims to examine the semi-peripheral positions of Quebec and Swedish literatures, analyzehow these positions influence the texts produced from these spaces, and expose the literary andextra-literary strategies employed by the authors to facilitate the transnational circulation of theirworks. The approach to marginalities within the corpus focuses on the ethical and political issuesraised by the act of representing figures from the margins in fiction. This leads us to closely studythe ethical positions negotiated and conceived within and outside the texts in relation tomarginalized figures, particularly their voices. We also analyze the political act of extendingcommon space in fiction by placing marginalized individuals at the center of the narrative.Beyond the boundaries of the corpus, the dissertation aims to consider how contemporary fictionalliterature represents marginality by drawing on ethical principles and political concerns, whilealso taking into account the risks and questions that these representations raise.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".