Bibliographic record
Abstract
Joanne was a tall woman with striking blue eyes and a manner that was simultaneously shy and defiant. I was to be her new family doctor. Her endocrinologist continued to prescribe the hormones supporting her transition from a male to a female. And, most importantly, she came with a skilled therapist helping her with the loss of her family who couldn't cope with her transgendered identity—and the fear of losing her twelve-year-old son who had just found out his father would prefer to live life as a woman. Her son meant the world to her. She was terrified of not accomplishing the tasks she had set herself: become comfortable as a woman, retool her job skills, support her son, and grow into her new life. Joanne's visits focused on health maintenance and several minor complaints, including a popping in her ears. With a normal physical exam, my first diagnosis was somewhat dismissive. “I don't think its anything—it should clear up on its own.” She was back three weeks later. I examined her again—gave her a more fancy diagnosis: Eustachian tube dysfunction and suggested she try decongestants. “Sometimes this takes a while to resolve,” I warned her as she left. One month later she returned—“you have to do something about my ears—they are driving me crazy.” I felt impatient. Clearly she was overreacting. On the scale of things, her problem was pretty minor. I could have slowed down long enough to explore her concern, however, in my annoyance, I countered with a specialist referral. The ENT note comes back: Eustachian tube dysfunction. We were now on our fourth visit in as many months for the popping ears problem. I was armed with the note from the specialist. I gave her the spiel again (complete with a diagram): self-limited symptoms, no easy cure, try another allergy medication, etc. She was visibly unhappy, but I was too full of my own irritation to really pay attention. In a somewhat sharp tone, I said, “Clearly I am missing something, here—why are you so upset about your ears?” “It's my voice,” she said slowly, her eyes fixed on a spot where the floor meets the wall. “My voice—it's the most unfeminine thing about me. I'm trying so hard to train my voice … so I can sound like a woman … so that people will believe me and I can get on with my life. I can't hear my voice when my ears are popping … my voice makes me sound like a freak.” I was dumfounded—at my own arrogance, mostly. I had broken my own rule about never assuming I knew better than my patients about what was important in their lives. I had decided that popping ears were too trivial—and very nearly missed something that was of core importance to Joanne. Joanne was a very gracious teacher to me that day. It's a lesson I suspect I will have to keep on learning.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.026 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
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".