Attention, Representation, and Unsettlement in Katherena Vermette’s The Break, or, Teaching and (Re)Learning the Ethics of Reading
Bibliographic record
Abstract
Theories of literary ethics often emphasize either content or the structural relationship between text and reader, and they tend to bracket pedagogy. This essay advocates instead for an approach that sees literary representation and readerly attention as interanimating and that considers teaching an important aspect of an ethics of reading. To support these positions, I turn to Katherena Vermette’s 2016 novel The Break, which both represents the urgent injustice of sexualized violence against Indigenous women and girls and also metafictionally comments on the ethics of witnessing. Describing how I read with my students the novel’s insistent thematization of face-to-face encounters and practices of attention as an invitation to read with Emmanuel Levinas and Simone Weil, I explicate the text’s self-aware commentary on both the need for readers to resist self-enlargement in their encounters with others’ stories and also the danger of generalizing readerly responsibility or losing sight of the material realities the text represents. I source these challenges both in the novel and in my students’ multiple particularities as readers facing the textual other. Ultimately, the essay argues for a more careful attention to which works we bring into our theorizing of literary ethics, and which theoretical frames we bring into classroom conversations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".