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Record W4392063611 · doi:10.1017/9781788216258.027

Animals, pandemics and climate change

2023· other· en· W4392063611 on OpenAlexaboutno aff
Jeff Sebo, Lauren Van Patter

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicClimate changeGeographyCoronavirus disease 2019 (COVID-19)ClimatologyBiologyEcologyGeologyMedicine

Abstract

fetched live from OpenAlex

In 2020, Covid-19, the Australia bushfires, and other global threats served as vivid reminders that human and nonhuman fates are increasingly linked. Human use of nonhuman animals contributes to pandemics, climate change, and other global threats which, in turn, contribute to biodiversity loss, ecosystem collapse, and nonhuman suffering. This conversation coincided with the publication of Jeff Sebo's book, Saving Animals, Saving Ourselves . The conversation foregrounds the incalculable harms we inflict on non-human animals by causing or allowing countless of them to suffer and die for our own benefit and by driving many species to extinction and many ecosystems to collapse. In so doing, we are also serving to endanger our own future on this planet. JEFF SEBO is Clinical Associate Professor of Environmental Studies, Affiliated Professor of Bioethics, Medical Ethics and Philosophy, and Director of the Animal Studies MA Program at New York University. His research interests include moral philosophy, legal philosophy and philosophy of mind; animal minds, ethics and policy; AI minds, ethics and policy. LAUREN VAN PATTER is the Kim & Stu Lang Professor in Community and Shelter Medicine in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph. She is an interdisciplinary animal studies scholar whose research focuses most broadly on questions of “living well” in multispecies communities. Lauren Van Patter ( LVP ): There is a widespread feeling that we can do much better than we are currently doing in our relationships with non-human animals. At the same time, however, many of us feel overwhelmed by the complexity involved in thinking about our responsibility to other animals. For example, when thinking about climate change, the situation is not so clear-cut that we can unequivocally say that climate change is bad for other species, while rewilding is good, both at the individual and the species level. How can we get past some of this paralysis around the immense complexity of these issues? Jeff Sebo ( JS ): There are two things that I think are true, but holding both in our heads at the same time is really difficult, because it creates a lot of tension. One is to accept that we have a responsibility to address factory farming, deforestation, the wildlife trade, and so on. We have to significantly regulate or abolish these industries that are causing so much harm to humans, to other animals, and to the climate.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0050.006
Open science0.0010.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.074
GPT teacher head0.334
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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