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Record W4229657595 · doi:10.1093/pastj/gtab041

Frontiers of Civilization in the Age of Mass Migration from Eastern Europe

2021· article· en· W4229657595 on OpenAlexaboutno aff
Cristina Andreea Florea

Bibliographic record

VenuePast & Present · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)EmpireDestinationsState (computer science)PeasantSovereigntyCivilizationHeading (navigation)GeographyPolitical scienceEconomyEconomic historyHistoryAncient historyPoliticsEconomicsArchaeologyTourismLaw

Abstract

fetched live from OpenAlex

Abstract Between the 1870s and 1914, tens of thousands of peasants left Austria-Hungary’s easternmost provinces of Galicia and Bukovina, heading for the Americas. This article places this episode in the context of contemporary global labour migrations while also emphasizing the distinctive characteristics of this mass exodus. Unlike most migrants around the world, Galicians and Bukovinans emigrated overseas rather than internally; their destinations included the United States, Canada, Brazil and Argentina. By moving, the migrants transformed from objects of Austria’s ‘civilizing mission’ in its eastern borderlands into vehicles for multiple, competing imperial expansion and civilizing projects overseas. From an obstacle to Austria’s ambitions to modernize its eastern periphery, the peasant migrants turned into a disputed resource, simultaneously expanding and threatening Austria’s sovereignty. Paradoxically, because they were less economically developed and more peripheral than their counterparts elsewhere in the empire, Galicians and Bukovinans were more sensitive to shifts in global labour markets than to the policies imposed by their own state officials.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0040.004
Open science0.0000.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.273
Teacher spread0.243 · 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 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
Published2021
Admission routes1
Has abstractyes

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