Bibliographic record
Abstract
COVID-19 and the prospect of a sixth mass extinction, among other crises, have underscored the urgency of recognizing humans' ethical and ecological entanglements with the more-than-human world. In light of these entanglements, this article brings together theories of occupational justice with those of animal rights in order to stimulate further discussion toward the development of a multispecies theory of occupational justice. First, a precedent and basis for nonhuman occupational rights is established in Martha Nussbaum’s capabilities approach. Second, Sue Donaldson and Will Kymlicka’s political theory of animal rights is invoked as a lens through which to understand how various types of nonhuman animals are differentially at risk for experiencing four occupational injustices: occupational deprivation, alienation, displacement, and apartheid. The article concludes that a commitment to multispecies occupational justice changes how science is practiced. Implications for occupational science and adjacent design sciences are explored, as well as directions for future research and political work.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".