Language “like a thousand little stars on the trees and on the grass”: Environmental inscription in Frances Brooke’s The History of Emily Montague
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".