A Network Analysis of Postwar American Poetry in the Age of Digital Audio Archives
Bibliographic record
Abstract
From the New American Poetry to New Formalism, publishing networks such as literary magazines and social scenes such as poetry reading series have served as a capacious mod-el for understanding the varied poetic formations in the postwar period. As audio archives of poetry readings have been digitized in large volumes, Charles Bernstein has suggested that open access to digital archives allows readers of American poetry to create mixtapes in different configurations. Digital archives of poetry readings “offer an intriguing and powerful alternative” to organizing practices such as networks and scenes. Placing Bern-stein’s definition of the digital audio archive into contact with more conventional under-standings of poetic community gives us a composite vision of organizing principles in postwar American poetry. To accomplish this, we compared poetry reading venues as well as audio archives — alongside more familiar print networks constituted by poetry an-thologies and magazines — as important and distinct sites of reception for American poet-ry. We used network analysis to visualize the relationships of individual poets to venues where they have read, archives where their readings are stored, and text anthologies where their poetry has been printed. Examining several types of poetic archives offers us a new perspective in how we perceive the relationships between poets and their “networks and scenes,” understood both in terms of print and audio culture, as well as trends and chang-es in the formation of these poetic communities and affiliations. We suggest that this ap-proach may offer new ways of imagining the multiple dimensions contributing to the so-cial formation of contemporary American 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".