‘Or’ in <i>Paradise Lost</i>: the Poetics of Incertitude Reconsidered
Bibliographic record
Abstract
Abstract Several Miltonists, beginning with Virginia Mollenkott in 1974, have identified ‘or’ as a key word in Paradise Lost because it is the word most associated with choice. In recent years, Peter C. Herman has made the bolder claim that ‘or’ is the poem’s key word because it refuses to choose. Herman argues that Paradise Lost is characterized by a ‘poetics of incertitude’ calculated to frustrate the reader. Whilst agreeing with earlier critics that ‘or’ is a key word in the poem, this essay challenges the claim that it always refuses (or even consistently offers) choices. A series of close readings demonstrates that Milton frequently uses ‘or’ to affirm, not waver. In ‘cletic’ (summoning) hymns, epic catalogues, and oratorical speeches, ‘or’ most often signals certainty, not incertitude. In the debate in Hell, every orator uses ‘or’ to pressure his audience to assent, not vacillate. The present essay is not, however, an attempt to replace Herman’s ‘poetics of incertitude’ with a rival ‘poetics of certainty’. The emphasis is on the diversity of ‘or’ in Paradise Lost. ‘Or’ sometimes signals uncertainty, but Milton never values incertitude for its own sake. The essay concludes by examining the poem’s cautious ventures into theology and astronomy, where Milton does keep his options open with ‘or’, but even in these cases he sometimes hints at a preference between the choices he declines to make.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".