Bibliographic record
Abstract
The view that non-human animals are ‘co-workers’ is a common trope used by researchers and the farming community, and increasingly forms the centre of inquiry in sociology, philosophy, and political economy. Scholars like Barbara Noske, Jocelyne Porcher, and Diane Stuart claim that animals are alienated from their labour, and that their contributions to our society are not recognized by it. Building on these findings, moral and political philosophers have recently argued that animals should have rights at work, like the right to remuneration or retirement. The much more pressing question, however, is whether animals should have a right to work. The right to work has emerged from a desire to recognize workers’ ‘right to pursue happiness’, and analogously, animals may have an interest in flourishing and in contributing to the wellbeing of their kin, which may be satisfied by fulfilling work. But the right to work is not without risk since it has been interpreted as a duty to work, is accused of reinforcing ableism and promoting dependency. This article provides an overview of the emerging debate, offers critical perspectives on the promises and pitfalls of animal labour, and establishes the necessary safeguards for labour to pave the way for interspecies justice.
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 | 0.037 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.101 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.016 |
| 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".