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Record W4395091801 · doi:10.3138/uhr-2023-0023

Reinventing Budapest in a Post-Imperial Era: Symbolic Landscapes of the City Between the Two World Wars

2024· article· en· W4395091801 on OpenAlexvenueno aff
Erika Szívós

Bibliographic record

VenueUrban History Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryGeographyAncient history

Abstract

fetched live from OpenAlex

This article explores the ways interwar Budapest adapted to 20th-century challenges in a post-imperial context, highlighting the distinctive features of Budapest as a capital city during the decades following the fall of the Habsburg Monarchy and the subsequent breakup of historic Hungary. After reflecting on the historical background and making comparisons with other capital cities of the region, the investigation concentrates on the symbolic dimensions of urban space, showing how the dominant political ideologies of the interwar era were represented in the cityscape of Budapest. The designs of public spaces, street names, other toponyms, monuments, and memorials stand at the focus of the analysis, all interpreted as elements of an attempt to transform the city’s space into a coherent symbolic landscape. It is an important goal to highlight the continuities and discontinuities between the pre– and post–World War I period. Meanwhile, the study also pays attention to the factors that had little to do with the official ideologies of the day, namely, the changing paradigms of urban planning and architecture, the role of market forces and municipal endeavors, and the possibilities of public versus private real estate development. It concludes that Budapest was characterized by a striking dichotomy of tradition and modernity during the interwar period: while conservative worldviews and various shades of right-wing ideologies exerted a strong influence on the public space, radically modern architectural forms and new patterns of urbanity appeared as well, all simultaneously shaping the city.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.296
Teacher spread0.253 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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