Nous nous souvenons. La représentation des pensionnats "indiens" dans les romans de Michel Jean
Bibliographic record
Abstract
This thesis examines how literature - and more specifically, the novels of Innu author Michel Jean - contributes to the construction and transmission of individual and collective memory related to the “Indian” residential schools in Québec. Through a qualitative content analysis, the study explores how literary narratives serve as powerful tools in representing and preserving the lived experiences and intergenerational traumas of Indigenous communities. The first part of the research situates residential schools within their historical context in both Canada and Québec, establishing the institutional and cultural mechanisms of assimilation that shaped Indigenous lives. The second part draws on memory studies, engaging with key theoretical frameworks such as Maurice Halbwachs’ concept of collective memory, Jan and Aleida Assmann’s notions of cultural and communicative memory, as well as concepts of autobiographical and intergenerational memory. These frameworks are then connected to the role of literature - particularly Indigenous literature - as a medium through which memory is both expressed and received. The following literary analysis focuses on four novels by Michel Jean - Kukum (2019), Atuk (2022), Maikan (2021), and Tiohtiá:ke (2023) - highlighting how each text mediates between personal testimonies and collective memory, and how they contribute to a broader understanding of Indigenous history and trauma within both Indigenous and settler-colonial societies. The findings underscore the role of literature not only in preserving memory, but also in fostering dialogue, empathy, and recognition - essential steps toward reconciliation and the acknowledgment of Indigenous histories in Québec and beyond.
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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 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".