The Newfoundland Master Narrative and Michael Crummey’s <i>Galore</i>: An Interpretive Framework
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.044 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".