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Record W4319335701 · doi:10.1111/nana.12928

Liberalism, nationalism and federalism: Reflections on the life and work of Meïr Goldschmidt

2023· article· en· W4319335701 on OpenAlexafffund
Alex Grøn Johnsen, John A. Hall

Bibliographic record

VenueNations and Nationalism · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCentral European national history
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNationalismEmpireLiberalismNational identityFederalismExceptionalismDemocracyPoliticsPolitical scienceEthnic nationalismDanishSociologyPolitical economyLawEconomic historyReligious studiesHistory

Abstract

fetched live from OpenAlex

Abstract The Danish‐Jewish publisher Meïr Aron Goldschmidt was an active political voice in Denmark in the 1840s and 1850s during the crisis of the Danish Oldenburg monarchy, when the ‘Danish Empire’ was troubled by territorial defragmentation, succession crisis, foreign military threats, Danish–German ethnic tensions and calls for democratic reforms by National Liberals. This article reflects on Goldschmidt's life and works as he attempted to syncretise the inherent dualities of nationalism and liberalism. His vision became a Swiss‐inspired federalism that should create a shared national identity based on liberal democracy to reunite the ethnic groups of the ‘Danish Empire’. Ultimately, history took another course, and the nationalist path was taken with disastrous results for Denmark. However, Goldschmidt has left a legacy in his writings as a microcosm of the ideas of his time by trying to syncretise nationalism and liberalism, cosmopolitanism and nationalism and a Danish and Jewish national identity.

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)0.012
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.087
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0190.065
Scholarly communication0.0130.008
Open science0.0020.008
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.338
Teacher spread0.266 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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