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Record W2067132023 · doi:10.1086/649413

National States and International Science: A Comparative History of International Science Congresses in Hitler's Germany, Stalin's Russia, and Cold War United States

2005· article· en· W2067132023 on OpenAlexaff
Ronald E. Doel, Dieter Hoffmann, Nikolai Krementsov

Bibliographic record

VenueOsiris · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicTwentieth Century Scientific Developments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInternationalism (politics)Political scienceInternational relationsNazi GermanyLawState (computer science)UniversalismSociologyPoliticsEconomic historyHistory

Abstract

fetched live from OpenAlex

Prior studies of modern scientific internationalism have been written primarily from the point of view of scientists, with little regard to the influence of the state. This study examines the state's role in international scientific relations. States sometimes encouraged scientific internationalism; in the mid-twentieth century, they often sought to restrict it. The present study examines state involvement in international scientific congresses, the primary intersection between the national and international dimensions of scientists' activities. Here we examine three comparative instances in which such restrictions affected scientific internationalism: an attempt to bring an international aerodynamics congress to Nazi Germany in the late 1930s, unsuccessful efforts by Soviet geneticists to host the Seventh International Genetics Congress in Moscow in 1937, and efforts by U.S. scientists to host international meetings in 1950s cold war America. These case studies challenge the classical ideology of scientific internationalism, wherein participation by a nation in a scientist's fame spares the scientist conflict between advancing his science and advancing the interests of his nation. In the cases we consider, scientists found it difficult to simultaneously support scientific universalism and elitist practices. Interest in these congresses reached the top levels of the state, and access to patronage beyond state control helped determine their outcomes.

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)0.003
metaresearch head score (Gemma)0.004
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0060.006
Scholarly communication0.0060.005
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.037
GPT teacher head0.280
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.

Study designTheoretical or conceptual
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

Citations22
Published2005
Admission routes1
Has abstractyes

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