Bibliographic record
Abstract
This essay examines the persuasive elements of one of the most influential books of the current era in psychiatry: Peter Kramer’s 1993 Listening to Prozac. That book, a text laden with the value of the hyperthymic (optimistic, charismatic, confident)personality, has been praised for illuminating questions of mood and identity, and blamed for ushering in an era of “cosmetic pharmacology”—and for making Prozac an object at the center of promiscuous prescription. The essay revisits depression, Kramer’s signal concern, in a post/pandemic exigence when millions, perhaps billions, of people come to meet the diagnostic criteria for that “disorder.” In many cases, mental-illness diagnosis, as a rhetorical act and a speech act, shifts a problem from social conditions of precarity and inequity, for example, to personal conditions of pathology. How did Kramer participate in making a capacious and biological view of depressed mood so persuasive, and why does it matter that he did?
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".