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Record W4312219507 · doi:10.1093/res/hgac090

<i>Emma</i> Pastoral

2022· article· en· W4312219507 on OpenAlexaff
Karen Valihora

Bibliographic record

VenueThe Review of English Studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicJoseph Conrad and Literature
Canadian institutionsYork University
Fundersnot available
KeywordsPoeticsLiteraturePoetryNarrativeFriendshipReciprocity (cultural anthropology)Harmony (color)HistoryArtSociologyAnthropology

Abstract

fetched live from OpenAlex

Abstract This essay situates the repeating and echoing refrains of Austen’s Emma (1816)—of gruel, draughts, and fires; of ‘poor Miss Taylor’, and ‘poor little Harriet’—within the tradition of classical pastoral. It suggests that the pastoral forms Austen employs at once tend to escape notice and are crucial to understanding what the novel is about. The overhearing of Emma’s thoughts staged in the novel’s free-indirect narration, for example, is approached as a version of the third-person framing of first-person speakers to comic and ironic effect in the earliest pastorals, those of Theocritus, Virgil, and Ovid, as well as in Marvell’s Mower poems. Pastoral’s chief occasion concerns the encounter between poets and shepherds, sophisticated and simple, high and low—and readers and hearers. The many occasions of overhearing through which the novel includes its readers in its world, the friendship between Emma and Harriet, as between Knightley and Robert Martin, the speeches of Miss Bates, the reclusiveness of Mr Woodhouse—all suggest a pastoral poetics. The essay explores how the sense of a golden age of harmony and abundance that marks the daily round of life in Highbury foregrounds reciprocity, equity, and responsiveness, forms of recognition that challenge the rigid stratification and hierarchy of the world of which Austen writes.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.005
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.002

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 head0.037
GPT teacher head0.265
Teacher spread0.228 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Review of English StudiesSame topicJoseph Conrad and LiteratureFrench-language works237,207