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Record W2318232811 · doi:10.17742/image.tgvc.5-2.4

ASKEW POSITIONS—SCHIEFLAGEN: DEPICTIONS OF CHILDREN IN GERMAN TERRORISM FILMS

2014· article· en· W2318232811 on OpenAlexvenueno aff
Maria Stehle

Bibliographic record

VenueImaginations Journal of Cross-Cultural Image Studies · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicGerman History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsTragedy (event)GermanInnocenceUncannyPoliticsSymbol (formal)TerrorismRepresentation (politics)HistorySociologyNormativeLawLiteratureMedia studiesAestheticsPolitical scienceArtPhilosophyLinguistics

Abstract

fetched live from OpenAlex

This essay discusses the appearance of children in films that negotiate the legacies of West left-wing German and global terrorism. The four films discussed in this essay depict children in Schieflagen (askew positions), but use these images to create rather different political messages. InDeutschland im Herbst (1978), Die bleierene Zeit(1981) and Innere Sicherheit (2000), children are melodramatic devices that convey a sense of national tragedy, nostalgia for “innocence,” and/or a nationally coded sense of hope. As opposed to representing such an uncanny mixture between melodramatic victim and national symbol, children in the recent film collaborationDeutschland 09 (2009) are the face of the present. Deutschland 09 depicts children as disconnected from German history, which relieves them of the burden of national representation and, as a result, offers a potential for a less normative and more diverse perspective on Germany’s history and present. While their missing connection to national history leaves them to appear detached and confused, this confusion can be read as a search for different understandings of history and belonging in twenty-first century Germany.

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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.025
GPT teacher head0.355
Teacher spread0.330 · 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 routes1
Has abstractyes

Explore more

Same venueImaginations Journal of Cross-Cultural Image StudiesSame topicGerman History and SocietyFrench-language works237,207