From Miracle to Menace: Discussing the Class Divide of Substance Use
Bibliographic record
Abstract
A conversation with physician and author, Lydia Kang, to discuss her research related to substance misuse and its varied acceptance in society as seen in her writing, Opium and Absinthe, Quackery: A Brief History of the Worst Ways to Cure Everything, and A Beautiful Poison. Throughout history substance misuse has been a constant among all classes of people. Yet, the acceptability of “using” has changed with time, the substance, the user, and scientific understanding of the nature of addition. This social, moral and political conflict has played out in legislation and in fiction writing. Dr. Kang explores the overlaps and implications of the history, stigma, and acceptance of substance misuse as seen through her historical research, fictional characters, and her career as an internal medicine physician. This program is presented in conjunction with the exhibition Pick Your Poison: Intoxicating Pleasures & Medical Prescriptions, created by the National Library of Medicine and on display at McGoogan Library Lydia Kang is an author of young adult fiction, adult fiction and non-fiction, and poetry. She graduated from Columbia University and New York University School of Medicine, completing her residency and chief residency at Bellevue Hospital in New York City. She is a practicing physician and associate professor of Internal Medicine who has gained a reputation for helping fellow writers achieve medical accuracy in fiction. Her poetry and non-fiction have been published in JAMA, The Annals of Internal Medicine, Canadian Medical Association Journal, Journal of General Internal Medicine, and Great Weather for Media. She believes in science and knocking on wood, and currently lives in Omaha with her husband and three children.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.039 | 0.057 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.011 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".