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Record W4243324776 · doi:10.17645/mac.v1i1.74

The Nanking Atrocity: Still and Moving Images 1937–1944

2014· article· en· W4243324776 on OpenAlexaffabout
Gary Evans

Bibliographic record

VenueMedia and Communication · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicPhotography and Visual Culture
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEmotiveContext (archaeology)Public opinionVisual cultureMedia studiesWorld War IISociologyHistoryVisual artsLawPolitical scienceArtPoliticsArchaeologyAnthropology

Abstract

fetched live from OpenAlex

This manuscript investigates the facts of publication of the images of the Nanking Atrocity (December 1937–January 1938) in LIFE and LOOK magazines, two widely read United States publications, as well as the Nanking atrocity film clips that circulated to millions more in American and Canadian newsreels some years later. The publishers of these images were continuing the art of manipulation of public opinion through multimodal visual media, aiming them especially at the less educated mass public. The text attempts to describe these brutal images in their historical context. Viewing and understanding the underlying racial context and emotive impact of these images may be useful adjuncts to future students of World War II. If it is difficult to assert how much these severe images changed public opinion, one can appreciate how the emerging visual culture was transforming the way that modern societies communicate with and direct their citizens' thoughts.

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)0.002
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: none
Teacher disagreement score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.020
GPT teacher head0.230
Teacher spread0.210 · 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
Published2014
Admission routes2
Has abstractyes

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