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Record W2002181689 · doi:10.1353/eam.2015.0004

“Shelburnian Manners”: Gentility and the Loyalists of Shelburne, Nova Scotia

2015· article· en· W2002181689 on OpenAlexaboutno aff
Bonnie Huskins

Bibliographic record

VenueEarly American studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaIdentity (music)Settlement (finance)NegotiationHistoryArtLawPolitical scienceEthnologyAesthetics

Abstract

fetched live from OpenAlex

The Loyalist refugees who made their way to Shelburne, Nova Scotia, in the aftermath of the American Revolution have been denigrated in various accounts as Loyalist “layabouts” and “dancing beggars” who spent too much of their time and capital on genteel sociability and conspicuous consumption. The writings of three of the sharpest critics of Shelburne—the Scottish merchant James Fraser, the Loyalist surveyor Benjamin Marston, and British Royal Engineer William Booth—often appear in modern academic works without sufficient contextualization. This paper asserts that the commentaries of Fraser, Marston, and Booth are not merely critiques of Shelburne per se, but part of a larger trans-Atlantic debate about the dilution and democratization of gentility. Indeed, the uprooted populations that made their way to Shelburne took advantage of the fluidity of the pioneer settlement to negotiate a form of middling gentility that acted as a vehicle of social mobility and identity reformation.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.008
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.304
Teacher spread0.264 · 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 designQualitative
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

Citations2
Published2015
Admission routes1
Has abstractyes

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