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Record W4366419906 · doi:10.1080/01433768.2023.2196125

Visualising emptiness: the landscape of the Western Front and Australian and English children’s picture books

2023· article· en· W4366419906 on OpenAlexaboutno aff
Martin Kerby, Margaret Baguley

Bibliographic record

VenueLandscape History · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsnot available
FundersVlaamse regering
KeywordsWitnessWildernessAestheticsDystopiaHistoryEmptinessNothingFront (military)Power (physics)SociologyLawLiteratureGeographyArtPolitical sciencePhilosophyEpistemology

Abstract

fetched live from OpenAlex

Although the Great War made extraordinarily complex demands on the nations involved, it is the landscape of the battlefield which has continued to dominate contemporary perceptions of the conflict. Australian and English children’s picture book authors and illustrators have adopted a similar focus, particularly regarding the Western Front. It is the illustrators, however, who have the more complex task, for they have inherited an aesthetic issue that has challenged artists since 1914. Like the British, Australian, Canadian, and New Zealand official war artists of the time, they are confronted, at every turn, by the challenge of depicting a surreally empty landscape. It was not so much a landscape as the artists understood it before the war, but rather an anti-landscape, as though the war had annihilated Nature. What was left was a dystopian wilderness that bore witness to the destructive power of industrialised warfare. This article will explore how a selection of Australian and English children’s picture book illustrators respond to the emptiness of the battlefield landscape, or as Becca Weir so evocatively characterises it, the paradox of measurable nothingness.

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)0.001
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: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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