Experiencing Exclusion: Scholarship after Inquisition
Bibliographic record
Abstract
Abstract In 1952, while working on my Ph.D. at the University of Michigan, two men from the Department of State came to our apartment and picked up my passport and that of my husband, Chandler Davis. In 1962, Chandler was finally allowed to immigrate to Canada with his family and take up a professorship at the University of Toronto. The ten years in between were packed with politics: Chandler’s refusal to answer questions before the House Un-American Activities Committee (HUAC); his firing from the University of Michigan; his court case challenging HUAC; his six months’ prison term for contempt of Congress; and throughout, his inability to get a tenure-track appointment at any American university. I myself was not called to confess my political views or memberships but investigations against Chandler were based on the publication of Operation mind – a pamphlet which I had co-authored together with Elizabeth Douvan and which is presented here. I was without a passport and not part of a university community for years. This article reflects on the impact of this experience of persecution on my work as a historian, and the relationship between politics, activism, and what Marc Bloch and Lucien Febvre called ‘the historian’s craft’ and ‘consciousness’.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.036 | 0.060 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".