ââHere at the Brinkâ: Don McKayâs edge poetics and the articulation of wilderness in Canadian poetryâ
Bibliographic record
Abstract
The purpose of this study is to interrogate how select English Canadian poets have broached the unknowability of an elusive subject in the natural world, paradoxically articulating the unspeakable or ineffable. This study focusses most explicitly on the edge poetics of contemporary Canadian poet Don McKay. Reading McKay’s oeuvre for poems situated at or within edges, thresholds, and peripheries, this research analyzes how McKay’s poetry problematizes the ever-categorizing mind, thereby challenging our perception of the non-human other in his work. Drawing a line of influence back through the tradition from which he comes, this study first surveys a long history of Canadian nature poems (1888-1966). Examining five poems that confront the “inappellable” (Scott, “Height of Land”), the first chapter shows how five English Canadian poets (Lampman, D.C. Scott, Pratt, A.J.M. Smith, Atwood) play with a poetics of unknowing to resist articulation of the non-human other they describe. The second chapter investigates parallels between Al Purdy and Don McKay. Purdy’s poems create room to behold mysterium tremendum—a concept that parallels McKavian wilderness. In the third and final chapter, this study focuses on McKay’s edge poetics, revealing how his poems gesture at a non-linguistic space. I conclude by showing the ethical potential of this poetics of “unknowing” the other.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".