Scenes of Speculation: Abolition and the Movement Literatures of Black North America, 1783-1876
Bibliographic record
Abstract
Eighteenth- and nineteenth-century attempts to create re-settlement projects for free Africans in the Americas reveal how rhetorics of debt and investment were integrated into anti- slavery discourse from the beginnings of the United States. However, these re-settlement efforts have often been studied through the framework of nation-building and statecraft rather than from the vantage of common print and manuscript cultures of early African America. In Scenes of Speculation: Abolition and the Movement Literatures of Black North America, 1783-1876, I build a literary geography of early African American letters that focuses on how Black people wrote themselves into the narrative of U.S. expansion by asserting their right to be American as a right to own land, build credit, and hold property. My project re-casts Black nineteenth-century print and manuscript cultures as forms of “movement literature” that are always mapping their relation to abolition as a “for-us, by-us” activist project that measures its value in freedom. Ultimately I argue that by looking at early African American literature, we can track an enduring critique of American settler state that asserts that the right to remain in one’s adopted homelands and Black cultural sovereignty are fundamentally related forms of self-possession. I analyze how Black abolitionists leveraged property, landholding, and debt to further radical dreams of liberated futures, marking their belief that such investments would generate future value in the form of freedom rather than money. Across my dissertation I model an approach to early African American literature that tracks how free and enslaved Black people asserted their right to American citizenship by insisting that they had a substantive relationship with the land of the Americas itself. In my chapters I analyze genres varying from personal correspondence, petitions, poetry, novels, memoirs, speeches, periodicals, advertisements, portraiture, annual reports, ledgers, and journals. Over the dissertation I visit how locations such as Liberia, England, Canada, Mexico, Cuba, Sierra Leone, and Cherokee Nation factor into Black visions of hemispheric anti-slavery movements.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.027 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".