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Record W4320726929 · doi:10.1080/1369801x.2023.2169627

Social Encounters: Portraits of the Yup’ik Women of Taciq, Alaska, 1850–1851

2023· article· en· W4320726929 on OpenAlexaboutno aff
Eavan O’Dochartaigh

Bibliographic record

VenueInterventions · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersIrish Research CouncilScience Foundation IrelandEuropean Commission
KeywordsIndigenousPortraitEthnographyHistoryArcticPower (physics)The arcticEthnologyGenealogyGeographyArchaeologyEcologyOceanography

Abstract

fetched live from OpenAlex

During the mid-nineteenth century, over thirty maritime expeditions searched for the infamous missing Franklin expedition sent by the British Admiralty in 1845 that had vanished into the Northwest Passage. Several of these expeditions and individuals had extensive and sustained contact with Inuit, Yup’ik, and Chukchi people who lived in the region. The officers of these expeditions were required to keep accurate visual and written records of all that they encountered, while surgeons in particular were expected to keep details on natural history, including ethnographic information on Indigenous peoples of the Arctic. Many of these documents are overtly racist while others are underlain with less obvious, but highly pervasive, racist attitudes. Despite that, these records contain valuable, if flawed, information that can be of particular interest to Indigenous scholars and communities in the Arctic. Through examining written evidence and four watercolour portraits of women made at Taciq, Alaska, I show how such pre-photographic records can contain information that unsettles the assumed power dynamics between Indigenous peoples and agents of imperialism and can reveal traces of social encounters.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0200.008
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.086
GPT teacher head0.441
Teacher spread0.356 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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