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Record W3207772890

Dust and Fog, Fire and Salt: German Canadian Psychiatrist Karl Stern’s (1906–1975) Emigre Experience

2019· article· en· W3207772890 on OpenAlexvenueaboutno aff
Daniel Burston

Bibliographic record

VenueHistory of intellectual culture · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Research
Canadian institutionsnot available
Fundersnot available
KeywordsSternGermanMemoirConfessionalFeelingFaithJudaismHistoryReligious studiesPsychoanalysisSociologyArt historyLawTheologyPhilosophyAncient historyPsychologyPolitical sciencePoliticsArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Karl Stern (1906–1975) was a German-Jewish psychiatrist, who studied and worked alongside the neuropathologists Kurt Goldstein (1878–1965), Walther Spielmeyer (1879–1935), and Wilder Penfield (1891–1976). After fleeing Nazi Germany for London in 1935, he married and moved to Montreal in Canada in 1939, where he converted to Roman Catholicism in 1943. This article offers a close reading of pertinent passages and explores his memoir, The Pillar of Fire (1951), and his novel, Through Dooms of Love (1960), as well as In and Out (1989), a “confessional poem” by the Canadian classicist Daryl Hine (1936–2012), to demonstrate the feelings of powerlessness, isolation, and anonymity which Stern experienced after leaving Germany. These feelings had been complicated (on arrival in Canada) by ethnic antagonisms between Jews and Catholics at that time. It also explores and addresses Hine’s disparaging attitude toward Stern’s identification with his European heritage and his Catholic faith, offering an alternative interpretation of their presence.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.083
Threshold uncertainty score0.603

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0560.031
Scholarly communication0.0060.002
Open science0.0020.006
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0040.001

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 head0.031
GPT teacher head0.244
Teacher spread0.213 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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