Recursive Readings: Chaos, Curriculum, and Walt Whitman in “Specimen Days”
Bibliographic record
Abstract
In his 2005 postmodern novel “Specimen Days,” Michael Cunningham reads and re‐envisions Walt Whitman’s “Leaves of Grass” through three different stories in different genres, time periods, and landscapes. Each story, however, involves a set of repeating details, including character names and attributes, locations, a curriculum (of one kind or another) of “Leaves of Grass,” and the pedagogical figure of Walt Whitman. This article focuses on the process of reading and interpretation at work in Cunningham’s novel, modeled after Whitman’s own recursive processes in writing and editing “Leaves of Grass.”Poems, Whitman writes, “grow of circumstances, and are evolutionary” (1889/1973, p. 565). Whitman’s poems resist linearity and closure, employing contradiction as well as repetition: “Do I contradict myself? / Very well then I contradict myself, / (I am large, I contain multitudes)” (1891/1973, lines 1324‐1326). In “Specimen Days,” Cunningham also presents three visions of a non‐linear Whitman curriculum, often yielding strange and unpredictable results because of the poems’ resistance to fixed meanings. Cunningham’s recursive readings of Whitman through “Specimen Days” suggest possibilities for nonlinear interpretive practices and for viewing reading as a recursive process, a repeated search for meaning that in fact generates meaning in its iterations rather than finding it.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".