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Record W2770136288 · doi:10.15290/cr.2016.14.3.02

Michael Crummey’s River Thieves in the light of rescue history

2016· article· en· W2770136288 on OpenAlexaboutno aff
Ewelina Feldman-Kołodziejuk

Bibliographic record

VenueCrossroads A Journal of English Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPhilippine History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsRescue therapyArtMedicineInternal medicine

Abstract

fetched live from OpenAlex

Born and raised in Newfoundland and Labrador, Michael Crummey uses his inside knowledge to describe the region's peculiarities in vivid detail. All four of his novels are set in Newfoundland and weave a story of its inhabitants throughout different moments in the island's history. Though Crummey's prose is broadly characterized as historical fiction, his novels differ from their traditional counterparts. This article aims to invite a reading of Crummey's works through the prism of rescue history, a concept recently introduced by a Polish scholar, Ewa Domaska. Rescue history, drawing on frontier and post-colonial studies among others, is preoccupied with local, potential, existential and affirmative history whose goal is to rescue the future. Although the concept of rescue history encompasses a variety of disciplines and activities, this article will focus on the literary realization of the notion of rescue history in Crummey's debut novel River Thieves, published in 2002. Based on historical accounts of Captain David Buchan's expedition to Red Indian Lake, whose aim was to encourage trade and put an end to hostilities between English settlers and Beothuks, the novel encourages a compassionate revisiting of the chronicled events. Weaving an intricate web of human relations and dependencies, Crummey manages to restore agency to those who are situated on the periphery either due to gender, status or origin, thus reminding the reader that we are all capable of changing the course of history.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.799
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.039
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 teacher head, 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
Published2016
Admission routes1
Has abstractyes

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