Crossing Cultures and the Poetic Worlds of Forrest Gander, Thomas King, and Margaret Atwood
Bibliographic record
Abstract
Forrest Gander is a major American poet who crosses poetic, cultural, and linguistic bounds. This review article discusses the poetry and poetics of Gander in the context of two other poets, Thomas King and Margaret Atwood, providing a close reading of Gander’s Be With (2018), King’s 77 Fragments of a Familiar Ruin (2019), and Atwood’s Dearly (2020). King, an Indigenous writer and scholar born and educated in the United States, and Atwood, some of whose ancestors lived in the American colonies and who had been a student at Radcliffe/Harvard, also have American experience. Poets may be rooted in the local and national, but they are also part of a comparative or world poetics. These poems express their beauty, understanding, and wisdom in a world too often devoid of poetry. Nature underwrites culture, and the natural world pervades these three collections, which also address human feeling, especially grief and loss.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".