Bibliographic record
Abstract
As a vegetarian for several decades, Sue Hendler had a criterion for what could and could not be consumed: “Never eat anything that has a face.” Indeed, she once chided me, on those grounds, for eating shrimps. Her criterion exemplifies two important aspects of ethical decision-making. First, what ought to be done or not done depends upon what entities one is dealing with and deciding about. In other words, good ethics depends upon sound metaphysics; moral decision-making is, in part, a function of one’s ontology. Second, what something is (its ontology) to us as human beings—for example, a “being with a face”—partly depends upon how we relate to it, because how we relate to something makes some of its characteristics more salient than others, and even (in some cases) creates those characteristics. In other words, ontological identity is, in part, relational, and relating and relationships are core contributors to good ethical reasoning. This paper explores and elaborates upon these two fundamental claims, and shows how Sue Hendler supported these ideas in her life and in her work as a feminist planner.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.099 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".