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Record W1570549616 · doi:10.1080/18125440802085837

Licence for shooting:

2008· article· en· W1570549616 on OpenAlexaff
Monica Popescu

Bibliographic record

VenueScrutiny2 · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSouth African History and Culture
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhotojournalismCold warIdeologyState (computer science)HistorySpanish Civil WarPolitical scienceSociologyPhotographyMedia studiesGender studiesLawArtVisual artsPolitics

Abstract

fetched live from OpenAlex

ABSTRACT The war in Angola represented one of the hot spots of the Cold War. Despite the length of the conflict and the number of warring parties involved, relatively little attention has been paid to the ways in which South African literature represented this conflict. This article focuses on the relationship between text and image, between literature and photojournalism. For Etienne van Heerden (“My Cuban” and “My Afrikaner”) and Mark Behr (The smell of apples), literature is capable of exposing ideological regimentation, the role of state apparatuses in creating a captive audience, and the mechanisms that perpetuated apartheid mentality and endorsed South African foreign policies. These literary works, and their relationship to war photography, are also indicative of the relatively marginal yet revealing position South African cultural texts hold in global mediascapes focused on the Cold War.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.812
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.064
GPT teacher head0.305
Teacher spread0.240 · 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 designNot applicable
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
Published2008
Admission routes1
Has abstractyes

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