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Record W3035173642 · doi:10.14453/asj.v9i1.3

Should Animals Have a Right to Work? Promises and Pitfalls

2020· article· en· W3035173642 on OpenAlexfundno aff
Charlotte E. Blattner

Bibliographic record

VenueAnimal studies journal · 2020
Typearticle
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsnot available
FundersQueen's University
KeywordsFlourishingDutyPoliticsSociologyRemunerationEnvironmental ethicsWork (physics)Economic JusticeMoral imperativeAnimal rightsRight to workAlienationLaw and economicsLawPolitical scienceSocial psychologyPsychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.444
GPT teacher head0.455
Teacher spread0.011 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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