Amnesia and Politics in the Mount Hope Cemetery: Toward a Critical History of Race and Gender
Bibliographic record
Abstract
Introduction: A Rhizomic Trail The graves of Susan B. Anthony and Frederick Douglass are near neighbors at Mount Hope Cemetery in Rochester, New York. To walk from one to the other, you climb and descend rambling slopes, moving around an ancient esker, now a paved driving lane, and formerly the road that native Seneca people used to travel between Lake Ontario and the Bristol Hills. Along the way, reading any given inscription might bring a jolt of recognition, so crowded is the place with luminaries and supporting actors. Most days, you will find yourself nearly alone but for the urban wildlife, in a vast settling garden undisturbed by its historical standing. As Mount Hope sits atop retired catacombs and moves with the bustle of groundhog burrows, so too within the story of Mount Hope, active recesses are as likely to be found as they are to be found sealed over. The history we customarily acknowledge in Mount Hope is a sprout, sustained by unseen networks. This essay is about one of them: the lines between Anthony and Douglass and their political causes, which extended into the dynamics at work in the 2016 presidential election, and into the social and political activities that have followed it. More specifically, I will focus on the racial inequality Douglass and Anthony confronted, which lives on in our institutions, our appreciation of American history, and our tools of theoretical analysis. I utilize Friedrich Nietzsche's juxtaposition of three modes of interpreting history— monumental , antiquarian , and critical —to argue that the 2016 election and the predicaments in American social and political life that it laid bare indicate our need for a renewed critical history, as Nietzsche understood it and as Douglass practiced it, and for a racially relevant intersectional politics. The story of Anthony and Douglass's friendship includes Douglass's support of women's rights conventions, including in Seneca Falls in 1848, where his defense of the resolution for women's suffrage helped secure its inclusion in the convention's program. This story remembers that Anthony and Douglass were allies when he died, having just spent an afternoon together. Less discussed is the record of Anthony's indignation at the possibility that black men would win suffrage before white women and their consequent falling-out over the Fifteenth Amendment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.042 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".