Bibliographic record
Abstract
This article explores the potential of recognizing ethical obligations to the other-than-human world. In particular, I emphasize how emotional responses to other-than-human beings reflect a proper apprehension of the moral landscape, which then allows ethical insights into our obligations towards others. Although this article overlaps with other work in environmental ethics, I specifically relate Margaret O. Little’s moral epistemology to our emotional experiences with the other-than-human to illustrate how a gestalt shift from “humans as apart from” to “humans as embedded within” complicates the moral picture of how we live with and in this world. I argue that when humans attend to our experiences with nature in an open and caring way, we can more easily and accurately ascertain the moral significance of the other-than-human parts of nature. Affective responses reveal important details of the moral landscape. Recognizing a reality of deep interrelatedness with the other-than-human world, our emotional responses to other-than-human beings enable us to appreciate moral obligations to care for the rest of nature and consider our relationality with the other-than-human world as a moral issue.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".