Bibliographic record
Abstract
I begin this essay with an autobiographical introduction to explain why I studied philosophy and how I came to work in bioethics. I then consider three ethical frameworks and practices that I adopted in my work in bioethics. I begin with the framework that John Rawls makes explicit, where the purpose of ethical theory is to set out aims and objectives to guide our responses to the world. Since this approach did not provide the guidance that I was looking for, I took up writing haiku as an ethical practice. I present here many examples of haiku that I wrote to pay attention to situations in life and bioethics. The hope was that paying attention would lead me to respond in better ways. Since this practice helped me more with attending than responding, I turned to a third framework. Here I explore John Dewey’s ethical framework. After characterizing this framework, I consider features that an associated practice needs to have. In a brief conclusion, I note some affinities between the second and third ethical approaches, and note that the last ethical framework adopted may just be the latest stage in trying out frameworks and practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.147 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".