Bibliographic record
Abstract
"This paper examines the ways Walt Whitman attempts to implant nationalism in readers of Leaves of Grass (1891-2). It focuses on five poems: "Starting from Paumanok," "Our Old Feuillage," "Song of the Broad-Axe," "A Song for Occupations," and "By Blue Ontario's Shore." The first chapter discusses how the repetitive image of travel in "Starting from Paumanok" gives the reader a sense of nationhood. I use Benedict Anderson's theory of nationalism to argue that the poet evokes the feeling of comradeship by describing his imaginary travel all over the States and directly addressing readers to invite them to Americans' historic journey. The second chapter analyzes how the motif of simultaneity and anonymity serves the poet's aim. I maintain that his presentation of America as a nation for the present and future in "Song of the Broad-Axe" and his catalogues of plants, animals, people, and places in "Our Old Fueillage" create a strong sense of sharing homogeneous time based on an imagined community. The third chapter shows how the poet's personification of America succeeds in arousing nationalistic feelings. I apply Richard Handler's theory of nationalism to the way the poet presents America as an individual, living entity to be protected and tries to propagate jingoism in "Starting from Paumanok" and "By Blue Ontario's Shore." The fourth chapter investigates the privileged position of the poet toward the reader. I use Michel Foucault's theory of power and state that Whitman appropriates what Foucault calls "technologies of pastoral power," which developed from the Christian tradition in the West, in order to preach to the reader the virtue of worshipping godlike America in "Starting from Paumanok," "A Song for Occupations," and "By Blue Ontario's Shore." I contend that Whitman secularizes the forms of pastoral power to serve as a pastor (pastoral) leading people (flock) to become servants of the sanctified nation."
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".