Small and Large Cultures: Individuality, the Collective, Conformity and the Period of the Cold War
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".