Bibliographic record
Abstract
California, Colorado, and Canada have recently enacted physician aid-in-dying (PAD) legislation. Almost one fifth of the US population now lives in a state where patients can ask their physician for assistance with ending their lives. How will that “option” play out in an aging society plagued by ever-increasing inequality? California is the most diverse locale to enact PAD as a routine practice. There are 1.75 million residents of color over the age of 65. A state-wide group convened by the UCSF Bioethics Program has worked to provide guidance about the End of Life Option Act’s implementation to clinicians throughout the state. This presentation will report on these efforts, highlighting the unique ethical challenges for patients living in residential long term care facilities and for providers caring for an aging populace.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".