Bibliographic record
Abstract
I became active in the Jewish community and interested in medicine at about the same time. I was in high school, and I became involved in my synagogue's youth group. I loved the friends I made and the programmes I attended. And although there were no formal educational sessions, over time I learnt by example the values of social activism, leadership, community, personal growth, and ethical development. In the same year that I became president of my youth group I met Dr Ipp. I used to run Dr Ipp's office when he was working on call on the weekends. One Saturday he took me to a movie after work. Just before Hamlet's major monologue he was paged. A woman's baby had fallen and lost consciousness for a moment. Dr Ipp insisted that she take the baby to the hospital, but the woman refused, saying that she didn't want to drive on the Sabbath. Although in Judaism the saving of a life takes precedence over the customs of Shabbat, the woman continued to refuse. “Come on,” Dr Ipp said to me, “it's not a child's …
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".