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Record W4226358141 · doi:10.1525/phr.2022.91.2.190

Seeing Japan

2022· article· en· W4226358141 on OpenAlexaboutno aff
Benjamin Bryce

Bibliographic record

VenuePacific Historical Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicAsian American and Pacific Histories
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentation (politics)PhotographyContext (archaeology)HistoryVisual artsSociologyLawPolitical scienceArtArchaeologyPolitics

Abstract

fetched live from OpenAlex

This article examines the photography and writing of Cooper Robinson, who worked as a missionary in Japan between 1888 and 1925. Drawing from over 4,000 images, it analyzes how one missionary represented Japan, his religious project, and his personal life to Canadian audiences. Whether he used photographs to animate his lectures about Japan while in Canada on furlough, sent them as postcards to coreligionists, or saved them for his own memories, the question of representation was intimately tied to the question of audience. This article argues that Robinson’s photography and the broader Canadian missionary interaction with Japan in the late nineteenth and early twentieth centuries played an important role in shaping the vision that Canadians had of the country. The positive yet selective image that this missionary fostered of the country co-existed with widespread anti-Japanese agitation in North America; these photographs and the support for missionary work that they helped garner should be read with that broader context in mind.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.229
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.002

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 head0.024
GPT teacher head0.290
Teacher spread0.266 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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