Faction Figure: James P. Cannon, Early Communist History, and Radical Faith
Bibliographic record
Abstract
In his wide-ranging portrait of American life published in 1938, the leftliberal writer Louis Adamic recounted the tale of his research trip to the small Pennsylvania mining town of Shamokin while on assignment for The Nation magazine. In 1932, he learned, the Communist Party had dispatched its cadre to the state’s anthracite region to organize the unemployed. The leftist missionaries from New York and Philadelphia insisted that revolution in the United States was “imminent.” As good communists, they supported the “immediate and complete overthrow of capitalism,” championed “the proletariat and the dictatorship thereof,” viewed Soviet Russia as the “sole immediate ... [source] of all hope for humankind,” and “recognized ‘revolution’ in every strike threat and in every grumble against a wage cut.” If “one could judge by their talk and publications,” Adamic learned from locals, “they hated nearly everything in the United States.”1 The communists, many of them recent converts, were initially impressed with what they saw. Local coal bootleggers – men who illegally mined and sold coal from “company-owned lands, for the most part in open daylight, by the most primitive methods imaginable, in complete disregard of private property rights and successful defiance of company police,”2 had organized an Independent Coal Producers union. To communists’ eyes, a bootlegger of
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".