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Record W2573716059 · doi:10.1017/s0008938916000662

Mapping the Red Threat: The Politics of Exclusion in Leipzig Before 1914

2016· article· en· W2573716059 on OpenAlexaff
James Retallack

Bibliographic record

VenueCentral European History · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoliticsDisadvantageVotingDemocracySuffragePolitical sciencePolitical economyPower (physics)Social Democratic PartyLawSociologyPublic administration

Abstract

fetched live from OpenAlex

Abstract Long before Adolf Hitler’s appearance clouded democracy’s prospects in Germany, election battles had provided a means to disadvantage “enemies of the Reich” in the polling booth. Such battles were waged not only during election campaigns but also when new voting laws were legislated and district boundaries were redrawn. Maps produced during the Imperial era informed voters, statesmen, and social scientists how the principle of the fair and equal vote was compromised at the subnational level, and new maps offer historians an opportunity to consider struggles for influence and power in visual terms. This article argues that local, regional, and national suffrages need to be considered together and in terms of their reciprocal effects. On the one hand, focusing on overlaps and spillovers between electoral politics at different tiers of governance can illuminate the perceptions and attitudes that are constitutive of electoral culture. On the other hand, using cartography to supplement statistical analysis can make election battles more accessible to nonspecialist audiences. Combining these approaches allows us to rethink strategies of political exclusion in Imperial Germany’s coexisting suffrage regimes. Focusing on Leipzig and its powerful Social Democratic organization opens a window on larger issues about how Germans conceived questions of political fairness in a democratizing age.

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: none
Teacher disagreement score0.829
Threshold uncertainty score0.482

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.045
GPT teacher head0.243
Teacher spread0.198 · 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
Published2016
Admission routes1
Has abstractyes

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