Speaking the unspeakable; or providing the evidence without being censored
Bibliographic record
Abstract
This article is about the difficulties inherent in using the racist tropes resulting from the transatlantic slave trade to address the chronic persistence of systemic racism. The problem with revealing horrific material – such as the fugitive slave ads I cite from early nineteenth-century Barbados newspapers – is that they raise the risk of causing offence. Yet the point of speaking the unspeakable is to move towards telling truths revealing both the brutality of enslavers and the ingenuity and courage of enslaved individuals who resisted. By focusing on the heroism of people in the fugitive slave ads I shift attention away from the White legislators typically credited with abolition and towards people who consistently resisted enslavement. My account of navigating the treacherous territory of speaking the unspeakable resolves as a cautionary tale about making sure that unspeakable, long concealed material is buffered with trigger warnings and careful explanations as to why it is being revealed.
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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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".