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Record W3034364432 · doi:10.3138/jcs-2019-0019

The Newfoundland Master Narrative and Michael Crummey’s <i>Galore</i>: An Interpretive Framework

2020· article· en· W3034364432 on OpenAlexvenueaboutno aff
Kristina Fagan Bidwell, Jessica S. McDonald

Bibliographic record

VenueJournal of Canadian Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeDepictionSociologyPersonaFraming (construction)Diversity (politics)AestheticsPopulationHistoryLiteratureArtAnthropologyHumanities

Abstract

fetched live from OpenAlex

This article presents a framework for identifying and analyzing the Newfoundland master narrative, a narrative that is powerful and persistent in Newfoundland literature, its marketing, and its popular and critical reception. This master narrative problematically positions Newfoundlanders as timeless and authentic but also moving inexorably toward loss and disappearance. As a representative case, this narrative is shown to be fundamental to the novels, public persona, and reception of Michael Crummey, one of Newfoundland’s most popular and acclaimed writers, focusing on his 2009 novel Galore. Through an analysis of Galore and Crummey’s framing authorial comments, the figures, structures, and perspectives that are fundamental to the Newfoundland master narrative are identified. While Crummey’s novel does contain diverse characters, and while he has publicly affirmed the diversity of the Newfoundland population, we show that his depiction of diversity ultimately affirms the exclusionary master narrative. And, in this, he is part of a wider tendency, even among those who explicitly critique the Newfoundland master narrative, to overlook its presence, underestimate its power, or use it despite themselves. This interpretive framework is thus intended to expose and to critically engage with the often hidden workings of the Newfoundland master narrative.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.289
Teacher spread0.247 · 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 teacher head, not a consensus.

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
Published2020
Admission routes2
Has abstractyes

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