Science AMA Series: I’m David Roth Singerman, here to talk about the history of the science of sugar, AMA!
Bibliographic record
Abstract
I’m a historian of science, technology, the environment, and American capitalism. I have a PhD from MIT’s program in History, Anthropology, and Science, Technology, and Society, where my research was supported by the National Science Foundation and the Social Science Research Council. My dissertation, “Inventing Purity in the Atlantic Sugar World, 1860-1930,” was awarded prizes in 2015 for the best dissertation in business history in both the U.S. and Britain, and his work has been published in the Journal of the Gilded Age and Progressive Era, the Journal of British Studies, and Enterprise & Society, while another article is forthcoming in Radical History Review. I’m currently a visiting scholar at UVA and working on my first book Purity and Power in the American Sugar Empire, 1860-1940, which narrates a new history of U.S. imperialism by tracing material struggles over knowledge about sugar’s substance and value. Drawing on research in U.S., Cuban, and Hawaiian archives, Purity and Power shows how the U.S’s attempts to govern nature and human labor in its Pacific and Caribbean colonies were inseparable from contests over corruption, free trade, and corporate power at home. I’m also preparing an article about food, labor, and scientific knowledge in the 1880s and 1890s, examining scandals over the smuggling of frozen Canadian herring into Gloucester, Massachusetts. Before this, I was a postdoctoral fellow at the Rutgers Center for Historical Analysis and a research associate at Harvard Business School. Ask me anything about the history of science or technology! EDIT—thank you! This has been great fun. I hope my answers have been helpful and sorry I couldn’t get to all of your questions.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.093 | 0.034 |
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".