Creating a Community of Witnesses: Acts of Reading in Anne Michaels’s Fugitive Pieces
Bibliographic record
Abstract
This article considers the reading effects of the mise en abyme in Anne Michaels's Fugitive Pieces to create a community of witnesses among readers. The novel’s multi-voicedness, created through a series of narratees and narrators, models complex identifications of the author, narrators, and reader. Through the figure of the reader presented by the narratees Bella, Michaela, and Naomi, as well as the narrators Athos, Jakob, and Ben, Michaels engages us in acts of reading and interpreting the ongoing effects of the Holocaust. She offers a prime example of not the eyewitness but the reader as witness in recent Canadian fiction.Cet article examine les effets de lecture de la mise en abyme dans Fugitive Pieces d’Anne Michaels pour créer une communauté de lecteurs en tant que témoins. Le caractère multivoix du roman, créé par une série de narrataires et de narrateurs, modélise les identifications complexes de l’auteur, des narrateurs et du lecteur. À travers la figure du lecteur représentée par les narrataires Bella, Michaela et Naomi, ainsi que par les narrateurs Athos, Jakob et Ben, Michaels nous engage dans des actes de lecture et d’interprétation des effets continus de l’Holocauste. Elle offre un excellent exemple non pas du témoinoculaire, mais du lecteur en tant que témoin dans la fiction canadienne récente.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".