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A History of Hungarian Studies as Reflected in Forty-Seven Years of Scholarship: <i>Hungarian Studies Review</i>, 1974–2020

2021· article· en· W3179428861 on OpenAlexaffvenueabout
Árpád von Klimó, Leslie Waters, Steven Jobbitt

Bibliographic record

VenueHungarian Studies Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geopolitical and Social Dynamics
Canadian institutionsLakehead University
Fundersnot available
KeywordsScholarshipPoliticsHistoryRussian studiesÉmigréDiasporaState (computer science)Field (mathematics)Political scienceClassicsMedia studiesSocial scienceSociologyGender studiesLawSoviet union

Abstract

fetched live from OpenAlex

ABSTRACT Surveying forty-seven years of Hungarian Studies Review, this editorial essay examines some of the major scholarly trends within Hungarian Studies, an interdisciplinary field that took hold in North America after World War II. Energized by the contributions of émigré scholars who fled Hungary in the wake of the 1956 revolution, Hungarian Studies was later shaped by the collapse of state socialism in 1989. Tracing the evolution of the field across different generations of scholars, the essay reflects on the various contributions that Hungarian Studies Review and its precursor The Canadian-American Review of Hungarian Studies have made over the last five decades, not only to discussions of Hungarian politics and history but also to the study of art, literature, and culture, as well as life and community in the diaspora. Highlighting ways in which contributors have pushed the boundaries of the field, the essay also looks at how the journal has provided a forum for scholarship on women and gender, and for studies that, for political reasons, have not always been possible to pursue in Hungary itself.

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)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0140.026
Science and technology studies0.0030.006
Scholarly communication0.0090.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.112
GPT teacher head0.405
Teacher spread0.293 · 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.

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

Citations0
Published2021
Admission routes3
Has abstractyes

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