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Record W4392219606 · doi:10.1017/9789048551934

Finance and the World Economy in Weimar Cinema

2023· book· en· W4392219606 on OpenAlexaff
Owen Lyons

Bibliographic record

VenueAmsterdam University Press eBooks · 2023
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWeimar RepublicModernityCapitalismSpeculationMovie theaterCapital (architecture)FinanceEconomyArt historyPolitical scienceArtEconomicsPoliticsLawVisual arts

Abstract

fetched live from OpenAlex

After the First World War, the effects of financial crisis could be felt in all corners of the newly formed Weimar Republic. The newly interconnected world economy was barely understood and yet it was increasingly made visible in the films of the time. The complexities of this system were reflected on screen to both the everyday spectator as well as a new class of financial workers who looked to popular depictions of speculation and crisis to make sense of their own place on the shifting ground of modern life. Finance and the World Economy in Weimar Cinema turns to the many underexamined depictions of finance capital that appear in the films of 1920s Germany. The representation of finance capital in these films is essential to our understanding of the culture of the Weimar Republic - particularly in the relation between finance and ideas of gender, nation and modernity. As visual records, these films reveal the stock exchange as a key space of modernity and coincide with the abstraction of finance as a vast labour of representation in its own right. In so doing, they introduce core visual tropes that have become essential to our understanding of finance and capitalism throughout the twentieth century.

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)0.000
metaresearch head score (Gemma)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.189
Teacher spread0.165 · 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
Published2023
Admission routes1
Has abstractyes

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