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Record W2041639311 · doi:10.7202/019262ar

North of Boston: Models of Identity, Subjectivity and Place in the Poems of Robert Frost

2008· article· en· W2041639311 on OpenAlexvenueno aff
Stephen Regan

Bibliographic record

VenueRomanticism and Victorianism on the Net · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicModernist Literature and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsPoetrySubjectivityFrost (temperature)Identity (music)RomanticismHistorySubject (documents)American poetryLiteratureNew englandSubject matterArt historySociologyPhilosophyArtAestheticsLawGeographyEpistemologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Although the titles of Robert Frost’s collections of poetry, includingNorth of Boston, appear to ground his work in a precise location and a known community, the poems themselves belie any secure sense of geography and any secure sense of attachment. Many of the poems were, in fact, composed in Buckinghamshire and Gloucestershire shortly before the outbreak of the First World War, and they reveal an acute awareness of British, as well as American, literary traditions and ideals. This essay looks at how Frost created the New England of his poems, subtly establishing lines of continuity with British and American Romanticism while simultaneously harbouring profound philosophical doubts about inherited models of poetic subjectivity and imagination. The place of poetry, for Frost, is seen to be a place in which the play of mind is, itself, the most pressing subject matter.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.029
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.039
GPT teacher head0.226
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2008
Admission routes1
Has abstractyes

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