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Record W2753742404 · doi:10.5195/ahea.2017.313

Making Burgenland from Western Hungary: Geography and the Politics of Identity in Interwar Austria

2017· article· en· W2753742404 on OpenAlexaff
Ferenc Jankó, Steven Jobbitt

Bibliographic record

VenueHungarian Cultural Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsLakehead University
Fundersnot available
KeywordsAnnexationGermanPoliticsIdentity (music)Literal and figurative languageIdentity formationNazismPolitical scienceSociologyHistorySocial scienceArchaeologyAestheticsLinguisticsLawArtPhilosophy

Abstract

fetched live from OpenAlex

This study explores the role that geographical knowledge production played in the post-World War I “discovery” of Austrian Burgenland, focusing in particular on the relationship between geographical discourse and the politics of identity formation in the 1920s and 1930s. The primary task is to offer insight into this knowledge-making process by highlighting the discursive strategies employed in a variety of scholarly and popular texts, and by shedding critical light on the various actors and epistemic communities responsible for the imagining of Burgenland from its annexation to Austria in 1921 to the dissolution of the region and its subsequent re-invention as a Greater German border zone after the Nazi Anschluss of 1938. As Jankó and Jobbitt argue, Burgenland’s discovery between the wars was both figurative and literal. Whether the “discoverers” were Austrian or German, national or local, Burgenland was as much a discursive concept as it was a physical reality. Its emergent identity as a region, therefore, much like its actual borders, was fluid and often contested.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.011
Scholarly communication0.0070.004
Open science0.0000.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.109
GPT teacher head0.448
Teacher spread0.339 · 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 designQualitative
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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