Quests for the Power of the Poem (Forgetting the Power of the Past?)
Bibliographic record
Abstract
The review article examines three innovative books: Poetry Unbound by Mike Chaser looks at how to account for poetry in the context of past and contemporary social media, while Why Poetry by Matthew Zapruder and Don’t Read Poetry by Stephanie Burt look at how and why to read poetry in our contemporary culture. The article begins with a discussion of the evolution of poetry from the 1910s when poetry was subject to dramatic technological changes and the rise of radical art movements, attracting new audiences. As poetry became more open-ended, intertextual, playful, and exploratory, the question of what constitutes (good or bad) poetry came to the fore. In their collective defence of poetry, Chaser, Zapruder, and Burt champion poetry, whether accessible or challenging, as popular, relevant, and socially beneficial. Though the three books’ readings of poetry and the examples used are often conventional, in a sense unconscious of their radical forebears, each book in different ways asserts the ongoing presence and power of poetry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".