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Record W3028067982 · doi:10.5070/t22144902

Small and Large Cultures: Individuality, the Collective, Conformity and the Period of the Cold War

2020· article· en· W3028067982 on OpenAlexaffabout
Jonathan Locke Hart

Bibliographic record

VenueTerritories · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Cultural and National Identity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPeriod (music)Cold warTheme (computing)PoliticsNonconformityWrightLiteratureLawHistoryPhilosophyPolitical scienceArt historyArtAesthetics

Abstract

fetched live from OpenAlex

The Cold War is something I analyze in two parts. First, I examine its politics, including political literatures and cultures large and small that concentrate on central concerns of the Cold War. Second, I discuss small and minor literatures in the period of the Cold War in theory and practice, including examples from the Netherlands and Canada that are in the period of the Cold War but do not focus on it as its primary concern or theme. In these sections, I argue for the centrality of the tension between tyranny and liberty, individual and the group, conformity and nonconformity and related matters. The article ranges in the politics of the Cold War from the background of Marx and Mill though Churchill, Stalin, Truman, McCarthy to Russell, Grant and Ignatieff. In literature, that is the Cold War in ink, the essay analyzes Orwell’s essay on the nuclear bomb and his novels, Nineteen Eighty-four and Animal Farm as well as Miller’s play, The Crucible and a poem by Einstein on Russell. I concentrate on examples of Dutch fiction and their translation into English and a Canadian novel, The Weekend Man , by Richard B. Wright, because they are an element of “minority literatures.” Besides exploring the Cold War, I briefly examine theories of minor or small literatures, including some aspects of the views of Kafka, Deleuze and Guattari.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.042
Scholarly communication0.0110.007
Open science0.0010.007
Research integrity0.0010.004
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 head0.036
GPT teacher head0.275
Teacher spread0.239 · 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 designQualitative
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
Published2020
Admission routes2
Has abstractyes

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