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Record W1976524629 · doi:10.1177/0022009414551103

Unsettled Landscapes: Czech and German Conceptions of Social and Ecological Decline in the Postwar Czechoslovak Borderlands

2014· article· en· W1976524629 on OpenAlexaff
Eagle Glassheim

Bibliographic record

VenueJournal of Contemporary History · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCommunismGermanConversationWorld War IIHistoryCzechPolitical scienceEconomic historySociologyLawPoliticsArchaeology

Abstract

fetched live from OpenAlex

After the Second World War, Czechoslovakia expelled around three million Germans from the border region known as the Sudetenland. Close to two million Czechs and Slovaks moved from the country's interior to settle the region. Though cut off from their former homes by international borders soon hardened by the Cold War, expelled Germans vigorously engaged their homelands. They wrote odes to their lost Heimats, travelogues of trips through ‘Sudeten’ landscapes, critical essays on Czech management of the borderlands, and demands for restitution or return. For Czechoslovak settlers and communist officials, the specters of Germans past and present were persistent reminders of the shortcomings of resettlement and the need to consolidate conditions in the borderlands. This article traces an ongoing conversation from the 1940s to the 1980s among communist officials, Sudeten German expellees, communist reformers, and dissidents about the Czechoslovak borderlands. By the 1980s, most parties to the conversation had come to understand the borderlands in ecological terms, stressing the inter-relationship of mental, social, and physical geographies. Though communist officials and critics largely shared a diagnosis of borderland decline, there was a wide range of prescriptions for how to restore the region to ecological and social health.

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)0.002
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: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.305
Teacher spread0.270 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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