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Record W4230033534 · doi:10.1017/cbo9781139872072.019

The Invisibles

2015· book-chapter· en· W4230033534 on OpenAlexaboutno aff
Steven Lubet

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLiterature: history, themes, analysis
Canadian institutionsnot available
Fundersnot available
KeywordsConstitutionHistoryLawArtPolitical science

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.040
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.015
Scholarly communication0.0080.008
Open science0.0010.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0400.008

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.

Opus teacher head0.043
GPT teacher head0.188
Teacher spread0.144 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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