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Record W4282934192 · doi:10.1080/23801883.2022.2074507

Arctic Exploration and the Mobility of Phrenology: John Ross's Ethnographic Portraits of the Netsilingmiut

2022· article· en· W4282934192 on OpenAlexaboutno aff
Ingeborg Høvik

Bibliographic record

VenueGlobal Intellectual History · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Natural History
Canadian institutionsnot available
FundersNorges Forskningsråd
KeywordsArcticPhrenologyEthnographyNarrativeContext (archaeology)The arcticGeologistArt historyHistoryAnthropologyArchaeologySociologyArtGeologyOceanographyLiterature

Abstract

fetched live from OpenAlex

Analysing a set of ethnographic images and illustrations resulting from John Ross’s second voyage to find a Northwest Passage in 1829–1833, this article considers the ways in which Arctic exploration intersected with emergent scientific thinking about race and ethnicity in Britain. In particular, it examines how mobility impacted ideas of phrenology and scientific imaging in the context of the Arctic. As a practitioner of phrenology and member of the Edinburgh Phrenological Society, Ross’s expertise in this new mental science certainly travelled with him to the Arctic. As his field drawings and book illustrations testify, however, Ross’s knowledge was also affected by his immediate contact with the Inuit in Boothia Peninsula in Nunavut. Comparing Ross’s field drawings and illustrations in his two-volume Narrative and Appendix to their accompanying texts and to select ethnographic illustrations produced by his fellow Arctic explorers, this article uncovers the material and conceptual transformations Ross’s scientific visualisation of Inuit underwent during his physical movement between Britain and the Arctic.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.016
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.219
Teacher spread0.175 · 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.

Study designQualitative
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
Published2022
Admission routes1
Has abstractyes

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