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Record W2886846272 · doi:10.1111/japp.12337

Adapting to Climate Change: What We Owe to Other Animals

2018· article· en· W2886846272 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Philosophy · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsClimate changeEnvironmental ethicsDutyClimate justiceAdaptation (eye)Context (archaeology)Animal rightsAction (physics)Animal ethicsEconomic JusticePolitical scienceHuman rightsNon-humanSociologyLaw and economicsLawEcologyPsychologyGeographyBiologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract In this article, I expand the existing discourse on climate justice by drawing out the implications of taking animal rights seriously in the context of human‐induced climate change. More specifically, I argue that nonhuman animals are owed adaptive assistance to help them cope with the ill‐effects of climate change, and I advance and defend four principles of climate justice that derive from a general duty of adaptation. Lastly, I suggest that even if one can successfully argue that the protection of human interests in adaptation ought to be prioritised, nonhuman animal rights will continue to place significant constraints on climate change action.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.002

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 head0.052
GPT teacher head0.271
Teacher spread0.219 · 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