Bibliographic record
Abstract
Rushing to the hospital at 2 a.m., I tell myself, “No hurry, the baby is already dead.” Nevertheless I feel compelled to get there quickly. The drive is quiet, the city as sleepy as it ever gets. I am late for the blessed event. One of my specialist colleagues is cleaning up. The lighting is low, the manner funereal. The specialist speaks in hushed, compassionate tones. Then he leaves. I sit for a long time, offering my presence to the parents' grief and anger. They try to absorb the loss of their chromosomally normal child, killed by our need for reassurance. The nurse brings her in and places her in her father's arms. “I didn't think I would want to see her or touch her,” he says. Then he turns to me. “Carl, this is Kim.” His eyes lock on mine, brimming with tears. I become aware of the music being piped into the room: Nat King Cole singing “Unforgettable.” Later, after all the forms are signed, I return to say good-bye. Simply entering the room takes an effort of will. It seems to me a holy place, where the loss of a life is being honoured. Mother and father embrace in the middle of the room, Kim between them, and they waltz. Through a little speaker in the ceiling Sarah McLachlan sings: it's a long way down, it's a long way down, it's a long way down to the place where we started from ... Carl Wiebe Assistant professor Department of Family Practice University of British Columbia Vancouver, BC
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.461 | 0.298 |
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".