Bibliographic record
Abstract
When I imagine American public health problems in the abstract, I see my hometown of Memphis, Tennessee. I picture a school system so broken that it voluntarily surrendered its own charter. I picture a city recently ranked as the second-most dangerous city in the United States, with more than 900 violent crimes per 100,000 residents. I picture a city that only just lost the title for poorest large metro area, with a child poverty rate over 40%. I picture a city that, staggeringly, is ranked first both in food insecurity and in obesity, with a quarter of its residents unable to buy food during the year, but with more than 36% being obese. I picture the menu at my sister’s favorite restaurant, where macaroni and cheese is, unironically, listed as a vegetable. This picture could not have felt more different from what I saw on my medical school’s family medicine rotation. At the end of two weeks in suburban New England, I had met exactly two people of color. The only language barrier I encountered was a minor difficulty understanding the accents of an older couple from Poland. Everyone drove to the clinic, because everyone had a car, because everyone had a job. Certainly, some of the problems I encountered were similar to what you’d see in Memphis. There, as everywhere, people had diabetes, hypertension, depression, pain. But because of the wealthy, whitewashed nature of the clinic, I see now that I had largely let my guard down. I didn’t expect these patients to be uneducated, to have drug addictions. I didn’t expect them to be racist. Blinded by my own biases, perhaps, I had forgotten that those problems, too, are universal. My mentor and I were seeing a septuagenarian with diabetes when this illusion was finally shattered. My mentor had just gotten back from a nutrition conference, and he was excitedly trying to sell his patients on a “plant-based diet,” which was code for “basically vegan.” This patient was more skeptical than most, so we took turns proposing whole foods he might find tolerable. When I suggested brown rice, he smirked derisively and said, “Yeah, but rice messes with your eyes, don’t it?” My mentor looked confused, so the patient placed his fingertips at the corner of his eyelids, pulled them into a squint, and said: “It makes ’em look like a Chinaman. I don’t want to have Chinaman eyes.” As a white male, I don’t know how outraged I’m allowed to get in response to a comment like that. I did anyway. Still, I wasn’t his doctor, and the visit was almost over. I was never going to see this guy again anyway. I could almost have forgotten the entire ugly discussion. But the patient, determined to be memorable, had one more barb up his sleeve. My mentor had to step out of the room, so I stuck around to keep him company. He asked where I was from, and I told him, “Memphis, Tennessee.” He said: “Y’know, I’ve always wondered something about the South. I was hoping you could clear it up for me.” Eager to bury the hatchet—and always interested in talking about my hometown—I said, “Sure.” Screwing up his face into the same wicked grin, he asked, “If a man and a woman get divorced in the South, are they still brother and sister?” For about the first time in my life, I said nothing. Fortunately, my mentor came back a few seconds later, and the patient was soon out the door. When we got to our desks, my mentor acknowledged the unfortunateness of the patient’s racist remarks, and we processed it, albeit superficially. (I don’t think he heard the second offense.) Still, I wondered to myself … knowing what I now did, would I be comfortable seeing this patient again? Could I rely on myself to give careful, considerate thought to his overall well-being? Would I be willing to be his doctor? While these questions are still hypothetical for me, they are likely to become real at some point in my career, and when that moment comes, I hope that I am ready to say yes. There’s a French saying for the witty thing you wish you’d said at the time but didn’t think of until you were halfway down the stairs. It’s called l’esprit de l’escalier: “staircase wit.” I’ve often suffered from such second-guessing, and this episode was certainly no different. My initial reaction was an immature wish to say something hurtful back to him, almost a sort of acting out to get his attention, maybe even wound him a bit to show him how he’d wounded me. I later concluded that sometimes the most satisfying wit is the type that’s meaningful only to oneself. If I could replay that scene, after the patient asked me about divorces in Memphis, I think what I’d say is: “Y’know, you’d be surprised how much you have in common with people in the South. You can travel the whole country, but, for better or for worse, people are pretty much the same wherever you go.” Acknowledgments: The author would like to acknowledge Professor Hedy Wald, Dr. Ed Feller, and the family medicine clerkship at Brown University.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.566 | 0.260 |
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".