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Record W2506269004 · doi:10.1057/9781137001900_10

Language “like a thousand little stars on the trees and on the grass”: Environmental inscription in Frances Brooke’s The History of Emily Montague

2011· book-chapter· en· W2506269004 on OpenAlexaboutno aff
Emily Bowles

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2011
Typebook-chapter
Languageen
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsWildernessExoticismHistoryOrder (exchange)GenealogyAlterityArtArt historyLiteraturePhilosophyEcologyTheology

Abstract

fetched live from OpenAlex

W hen Frances Brooke’s heroine Arabella (Bell) Fermor describes “the verdure” of the landscape of Quebec as “equal to that of England,” 1 she sets up a series of interpenetrations among cultivation and wilderness, domesticity and exoticism, and containment and expansiveness. The dichotomous nature of the relationship between England and Canada that underscores The History of Emily Montague (1769) provides Brooke with a governing binary—Britain is culture, Canada is nature; England is a land organized around patriarchal order, Canada is ruled by a “commonwealth of women” 2 —that she explores throughout her novel in order to speak with rather than for nature, even as she (and her characters) colonized the Canadian wilderness. In the tenth letter of this epistolary novel, rendered as an exchange between Bell and her friend Lucy, Bell evokes a landscape that is simultaneously overwritten by markings of British culture, empire, and literature, 3 while resistant to the inscriptions and the signs of intelligibility that Bell and her friends make in order to textually and cognitively process the world around them. The language of the fireflies’ bodies “sparkling like a thousand little stars on the trees and on the grass” suggests some alterity to Bell, and her simulations of the world that she inhabits reveal a sense of otherness that typifies the British colonists’ relationship to nature. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.002
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.234
Threshold uncertainty score0.466

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0140.011
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.020
GPT teacher head0.183
Teacher spread0.163 · 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
GenreOther

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
Published2011
Admission routes1
Has abstractyes

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