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Family and Poetic Affinities of Bulmer, Burns, and Cleghorn

2025· article· en· W4416952832 on OpenAlexaffabout
Angus Cleghorn

Bibliographic record

VenueBishop–Lowell Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicScottish History and National Identity
Canadian institutionsSeneca Polytechnic
Fundersnot available
KeywordsPoetryBrotherBalladAncestorAdventureQueen (butterfly)Family treeBiography

Abstract

fetched live from OpenAlex

Abstract This article yokes family stories about Robert Burns as a literary, cultural, and amicable influence upon the Bishop and Cleghorn families. It delves into Burns’s Scotland as lived with the article author’s ancestor Robert Cleghorn; they shared verses, sung and dined together, and exchanged letters about poetry and farming in Edinburgh and Dumfries. The Cleghorn and Bishop families brought Burns’s poetic culture to Canada. Elizabeth Bishop’s grandfather, William “Pa” Bulmer, read Burns and the Bible in the Great Village family parlor during evenings, and Bishop inherited stories of Burns being read during the Maritime seafaring adventures of William Hutchinson, the brother of her grandmother “Gammie.” Bishop’s childhood absorption of Burns influenced her oft-expressed wanderlust, her use of ballad forms, and her unflinching inclusion of abjection in poetry. Bishop’s Bulmer family hailed from Yorkshire and sailed to Nova Scotia in the 1740s. Bulmer/Boomer migrations to Canada continued in the early 1800s, including the author’s Boomer ancestors, Michael and his daughter Mary Boomer, who married my great-great-grandfather Andrew Cleghorn. Boomer genealogy from the Bulmer and surrounding villages in Yorkshire extends back to the clan’s historical roots as Huguenots in France exiled by Louis XIV. These 500-year-old circuits reveal diasporic culture at work.

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)0.003
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: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.018
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.055
GPT teacher head0.279
Teacher spread0.224 · 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
Published2025
Admission routes2
Has abstractyes

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