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Record W2804789036 · doi:10.1080/2373566x.2018.1462093

Canoes, Modernity, and the Colonial Imagining of Progress

2018· article· en· W2804789036 on OpenAlexaff
Max Ritts, Kelsey Johnson, Jonathan Peyton

Bibliographic record

VenueGeoHumanities · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of ManitobaUniversity of British Columbia
Fundersnot available
KeywordsColonialismModernityRepresentation (politics)IndigenousNarrativeReading (process)HistorySociologyPhotographyAestheticsLiteratureVisual artsArtArchaeologyEpistemologyPoliticsPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

This article, part photo-essay and part treatise, explores the interplays of history, photographic representation, and archival circulation at work in colonial imaginings of coastal canoes. Inspired by the work of Walter Benjamin, we analyze the archival photographic record in British Columbia to challenge narratives of progress; instead, we insist that Benjamin’s notion of the “optical unconscious” provides a method for reading canoe photos against broader logics of representation and colonial modernity. Our empirical reading of the colonial archives produces a circumscribed account of the representation of the canoe as a persistent technology of Indigenous mobility, labor, and lifeways, but also as a technology, the “disappearance” of which was used to assert narratives of decline and colonial progress. We conclude by suggesting that this article could be read as part of an emerging convergence of critical visual methods within both human geographies of the sea and mobility studies. In this sense, we suggest that the photography-based methods taken here hold potential for growing geographical engagements in marine mobility studies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.160
Threshold uncertainty score0.317

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0100.051
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.322
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2018
Admission routes1
Has abstractyes

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