Making the Unseen Visible: A Metahistorical Analysis of Why the Role of Anti-Nazism and the Names of Significant Blacks, Bureaucrats, Feminists, Veterans, and Most Whistleblowing Moralists Are Absent from, or Minimally Noted in, Standard Histories of Bioethics
Bibliographic record
Abstract
In order to determine the moment at which the mutation in discourse took place, we must look . . . to the region where " things" and "words" have not yet been separated, and where-at the most fundamental level of language-seeing and saying are still one.We must reexamine the original distribution of the vis i ble and the invisible insofar as it is linked with the division between what is stated and what remains unsaid.-Michel Foucault, 1963 1 The historian's task is to transform presumptively factual data and information into words that form a coherent narrative about the past.Others may question their reconstruction of the past, as did pioneering Canadian bioethicist Benjamin Freedman (1951-1997) when he posed the question, "Where Are the Heroes of Bioethics?" 2 Historian David Rothman placed a wreath of heroism firmly on the head of one whistleblowing moralist, Henry K. Beecher, but there is little else that is heroic in the history of bioethics as standardly constructed.In contrast, the narrative constructed in this book answers Freedman's question by emphasizing the actions of the many heroes and heroines indicated in its subtitle: African Americans (like Jenkins and Wiley), the World War I and II veterans who founded modern medical ethics (Alexander, Ivy, and Pridham), a few bureaucrats (Kelsey), some commissioners (Jonsen and Lebacqz), feminists (Boston Women's Health Book Collective, and Downer), thinktank founders (Callahan, 11 Making the Unseen Vis i ble: A Metahistorical Analy sis of Why the Role of Anti-Nazism and the Names of Significant Blacks, Bureaucrats, Feminists, Veterans, and Most Whistleblowing Moralists Are Absent from, or Minimally Noted in, Standard Histories of Bioethics
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.015 | 0.061 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".