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Record W2663902368 · doi:10.1080/08865655.2017.1340848

Borderland Identities in Niagara: Craig Davidson’s <i>Cataract City</i> (2013)

2017· article· en· W2663902368 on OpenAlexaffvenueabout
Katherine A. Roberts

Bibliographic record

VenueJournal of Borderlands Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsNegotiationIdentity (music)FriendshipRepresentation (politics)Reading (process)ReservationSociologyState (computer science)LawPolitical sciencePoliticsAestheticsArtSocial science

Abstract

fetched live from OpenAlex

This present article explores how border identities are narrated in Craig Davidson’s Cataract City (2013), a Canadian novel about friendship between two young men, set in Niagara Falls, Ontario and their involvement with a smuggler on the Tuscarora Reservation near Niagara Falls, New York. Drawing on research on the complex and contradictory nature of border identities in Canada and elsewhere, it examines how the novel’s Niagara protagonists negotiate identity and relationships in the region. My reading shows how the text’s protagonists engage in cross-border activities without forming ties on the other side of the border. The Native characters demonstrate a different rapport with the 49th parallel, positioning themselves outside of both the American and Canadian nation-state yet without forming pan-tribal alliances. In the end, Davidson’s fictional representation of the Canada–U.S. border region in Cataract City confirms and complements border studies research that finds increasing obstacles to cross-border cooperation and an absence of shared identity constructs along the Canada–U.S. border.

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)0.002
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 score0.493
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.002
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 head0.057
GPT teacher head0.410
Teacher spread0.353 · 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

Citations2
Published2017
Admission routes3
Has abstractyes

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