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Record W4210358203 · doi:10.24043/isj.339

A man and his island: The island mirror in Michael Crummey’s Sweetland

2016· article· en· W4210358203 on OpenAlexaffvenueabout
Laurie Brinklow

Bibliographic record

VenueIsland Studies Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsSpace (punctuation)SoulBoundary (topology)Government (linguistics)HistoryGeographyGenealogySociologyPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Between 1946 and 1975, dozens of islands and outports in the Canadian province of Newfoundland and Labrador were abandoned as part of a government resettlement policy. Families and communities were torn apart, and a culture and way of life that revolved around the fishery changed irrevocably. The practice, which continues to this day, has been well documented, particularly by artists and writers. Michael Crummey’s 2014 novel Sweetland is a recent iteration. The relationship between humans and place is complex: on an island, with compressed space and a very real boundary that is the ocean, emotional attachments to one’s place are often heightened and distilled. What happens when a person is displaced from his or her island; when bonds of attachment are severed and one’s mirrored double is destroyed? Sweetland offers a fictional lens through which we see an example of a mirrored relationship between an island protagonist and his island setting. Exploring themes of attachment to place, and what Barry Lopez calls a “storied” or “reciprocal” relationship with the land, this paper examines what happens to a man when confronted with leaving an island he knows as deeply as his own body and soul; and how the island reacts.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.034
GPT teacher head0.318
Teacher spread0.284 · 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 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

Citations7
Published2016
Admission routes3
Has abstractyes

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