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Record W1754500077 · doi:10.3138/cbmh.31.1.41

Eugenics and Racial Biology in Sweden and the USSR: Contacts across the Baltic Sea

2014· article· en· W1754500077 on OpenAlexvenueno aff
Per Anders Rudling

Bibliographic record

VenueCanadian Journal of Health History · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Research
Canadian institutionsnot available
Fundersnot available
KeywordsEugenicsCommunismState (computer science)TreatyWorld War IIPolitical scienceNazi GermanyCold warSociologyEconomic historyGender studiesEthnologyHistoryLawPolitics

Abstract

fetched live from OpenAlex

The 1920s saw a significant exchange between eugenicists in Sweden and the young Soviet state. Sweden did not take part in World War I, and during the years following immediately upon the Versailles peace treaty, Swedish scholars came to serve as an intermediary link between, on the one hand, Soviet Russia and Weimar Germany, and, on the other hand, Western powers. Swedish eugenicists organized conferences, lecture tours, visits, scholarly exchanges, and transfers and translation of eugenic research. Herman Lundborg, the director of the world's first State Institute of Racial Biology, was an old-fashioned, deeply conservative, and anti-communist "scientific" racist, who somewhat paradoxically came to serve as something of a Western liaison for Soviet eugenicists. Whereas the contacts were disrupted in 1930, Swedish eugenicists had a lasting impact on Soviet physical anthropologists, who cited their works well into the 1970s, long after they had been discredited in Sweden.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0250.018
Scholarly communication0.0090.003
Open science0.0010.010
Research integrity0.0030.004
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 head0.069
GPT teacher head0.303
Teacher spread0.234 · 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 designObservational
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

Citations18
Published2014
Admission routes1
Has abstractyes

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