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Record W4244640406 · doi:10.3138/ecf.23.4.667

The Exchanged Portrait and the Lethal Picture: Visualization Techniques and Native Knowledge in Samuel Hearne's Sketches from His Trek to the Arctic Ocean and John Webber's Record of the Northern Pacific

2011· article· en· W4244640406 on OpenAlexvenueaboutno aff
Philippe Despoix

Bibliographic record

VenueEighteenth-Century Fiction · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPortraitInscribed figureThe arcticHistoryArcticArt historyGenealogyVisual artsCartographyArtGeographyOceanographyGeology

Abstract

fetched live from OpenAlex

Published accounts of the British circumnavigations from the 1770-80s effect the passage from complex knowledge inscribed in logbooks, astronomical and longitude calculations, charts, and natural history drawings to a new type of illustrated travelogue that associated the art of writing with techniques of visualizing the unknown. This model of maritime exploration and publication remained dominant for at least a century, obscuring other exploratory practices that will be investigated comparatively in this essay. I will contrast the uses of visual media in Samuel Hearne's trek through the plains of Canada (1769-72) with the artistic production developed by John Webber during James Cook's last voyage to the Pacific Ocean (1776-80). In comparing engravings from the two accounts, I will examine the ways in which different forms of expeditions and their specific visualizing techniques affect power relations during encounters as well as the subsequent production of knowledge. The different uses and appropriations of inscription techniques played a decisive role in the relationship established with the natives who were encountered by scientific maritime expeditions and by individual (or small team) explorations by ground.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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 designObservational
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
Published2011
Admission routes2
Has abstractyes

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