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Record W595709318

This Will Kill That?: Media Architecture

2009· article· en· W595709318 on OpenAlexaboutno aff
Edward Dimendberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGermanMovie theaterArt historyScholarshipModernityArchitectureMedia studiesCriticismWeimar RepublicArtSociologyHistoryLibrary sciencePolitical scienceVisual artsLawLiterature
DOInot available

Abstract

fetched live from OpenAlex

Edward Dimendberg is an associate professor of Film and Media Studies, Visual Studies, and German at the University of California, Irvine, and a University of California President's Research Fellow in the Humanities. He has received grants and fellowships from the German Fulbright Commission, the J. Paul Getty Trust, the Graham Foundation, the Canadian Centre for Architecture, the Social Science Research Council, and the International Research Center for Cultural Studies in Vienna. From 2005 to 2008 he served as the first Multimedia Editor of the Journal of the Society of Architectural Historians, and he remains a frequent lecturer at schools of architecture, museums, cinema studies programs, and film festivals. Dimendberg's book Film Noir and the Spaces of Modernity is a key contribution to scholarship on cinema and the city in the 1940s and 1950s. Together with Anton Kaes and Martin Jay, he coedited The Weimar Republic Sourcebook. As Sponsoring Editor in the Humanities at the University of California Press from 1990 to 1998, Dimendberg acquired and published manuscripts in philosophy, twentieth-century art, film studies, and European intellectual history. He is a general editor of the Weimar and Now: German Cultural Criticism book series and of the Flashpoints electronic book series.

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)0.002
metaresearch head score (Gemma)0.006
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: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.006
Scholarly communication0.0190.015
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.003

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 head0.026
GPT teacher head0.206
Teacher spread0.180 · 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
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
Published2009
Admission routes1
Has abstractyes

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