Bibliographic record
Abstract
This dissertation explores the ethical reading strategies at play in and impelled by the Canadian documentary long poem. I consider the ways in which poets encode and engage with the ethical problematics occasioned by their reconstruction of historical figures and events, arguing that documentary long poems encourage the reader to undertake a similar engagement—one that may result in the reader reaching drastically different conclusions than those apparently posited by a given work. Questions pertaining to appropriation, representation, and poetic license abound—questions that rarely, if ever, have stable answers. I contend that this indeterminacy is key to the ethical dimensions of the documentary long poem, as it generates open-ended dialogue regarding the recuperative work and potential harm that can result from the genre’s interrogative approach to historiography. In Chapter One I consider how Stephen Scobie’s McAlmon’s Chinese Opera invites the very ethical scrutiny that it thematizes via its representation of an historical subject who did not want aspects of his personal life transformed into literature. Chapter Two examines the documentary long poem’s capacity to offer the reader a dubious sense of historical veracity by looking to Patricia Young’s All I Ever Needed was a Beautiful Room and Douglas Burnett Smith’s Sister Prometheus, two works that link this capacity to the genre’s utilization of conventions associated with auto/biography. In Chapter Three I address the paucity of attention paid to Indigenous oral histories in critical engagements with the genre, reading Colin Morton’s The Hundred Cuts and Louise Bernice Halfe’s Blue Marrow as works that explore the damage wrought by a broader privileging of material evidence in historiographic considerations of Indigenous Peoples. Chapter Four reads Phinder Dulai’s dream / arteries and Jordan Abel’s Un/inhabited as works that outline different strategies by which archival holdings may be used to seek redress from the institutions and nation-states typically responsible for their maintenance. Throughout this dissertation, I frame the historiographic interventions of documentary poets as acts that invite scrutiny of not merely the contexts surrounding the production of historical records but also the uses to which such records are put by the poets themselves.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.042 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".