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Record W3014931503 · doi:10.1093/res/hgaa018

‘Or’ in <i>Paradise Lost</i>: the Poetics of Incertitude Reconsidered

2020· article· en· W3014931503 on OpenAlexaff
John P. Leonard

Bibliographic record

VenueThe Review of English Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Literary Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsPoeticsPoetryCertaintyParadiseKey (lock)Paradise lostLiteraturePhilosophyEPICAestheticsArtEpistemologyTheologyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.813
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.355
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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