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Record W2758752356 · doi:10.1017/s0008938917000619

The Death of News? The Problem of Paper in the Weimar Republic

2017· article· en· W2758752356 on OpenAlexaff
Heidi Tworek

Bibliographic record

VenueCentral European History · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWeimar RepublicNewspaperPoliticsHappeningDemocracyNothingPolitical economyGovernment (linguistics)Political scienceEconomic historySociologyLawHistoryArt history

Abstract

fetched live from OpenAlex

Abstract In the early 1920s, the press faced an existential challenge. Publishers proclaimed the death of news, not because nothing was happening, but because there was insufficient paper to print newspapers. While historians of the early modern period have long investigated material constraints on the spread of information, the problem of paper in Weimar Germany shows that the economics and politics of supply chains continued to shape cultural production in the twentieth century as well. Rationing during World War I subsequently became a crisis in the 1920s, when paper shortages, which had started as an issue of prices and supply chains, ballooned into a discussion about the role of the press in political and economic life, about the relationship between the federal states and the central government, and about the responsibility of a democratic government to ensure an independent press. Paper became a litmus test for the relationship between politicians and the press. The failure to resolve the crisis not only undermined the trust of publishers in Weimar institutions, but, this article argues, also enabled greater control by right-wing media empires. The public sphere, it turned out, had a very material basis.

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)0.004
metaresearch head score (Gemma)0.007
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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.008
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.051
GPT teacher head0.269
Teacher spread0.219 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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