Bibliographic record
Abstract
ON A SINGLE SUMMER AFTERNOON IN Oberlin, John Brown, Jr., had enlisted more black volunteers than his father was able to attract on any other occasion during the entire eighteen months that he was actively seeking troops for the Harper's Ferry operation. For the most part, the elder Brown failed badly in his efforts to recruit African-Americans. Both Harriet Tubman and Frederick Douglass declined to join him, and only one of the thirty-four black signatories of the Chatham Constitution eventually showed up at Harper's Ferry. Even Richard Richardson, a runaway slave who had joined Brown in Kansas and had participated in the Missouri rescue, remained in Canada rather than participate in the invasion of Virginia. Brown had figured heavily on the availability of black troops, depending on them to inspire local slaves to join his rebellion. As he proposed to Frederick Douglass, Brown had a “special purpose” in mind for his black comrades. “When I strike, the bees will begin to swarm, and I shall want you to help hive them.” Douglass demurred, later explaining that either “my discretion or my cowardice made me proof against the dear old man's eloquence.” But even without Douglass or another famous African-American, Brown was hopeful that ordinary black foot soldiers would guide an anticipated “swarm” of slaves to his emancipatory banner. Apart from Copeland and Leary, however, there would be only three blacks in Brown's small army. Why was the younger Brown so much more successful than his father at enrolling black men in their cause? Why did Copeland and Leary respond so readily when so many others equivocated or balked? Of course, we can only speculate – although we can surely assume that it was not a matter of superior persuasiveness, given the old man's renowned eloquence and charisma – but some tentative answers do suggest themselves. Oberlin, as we know, was a unique environment in the ante- bellum United States, combining an ideology of racial egalitarianism with an exceptional reverence for the rescue of slaves.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".