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Record W3012228850 · doi:10.3390/ani10030502

Humanity’s Best Friend: A Dog-Centric Approach to Addressing Global Challenges

2020· review· en· W3012228850 on OpenAlex
Naomi Sykes, Piers Beirne, Alexandra Horowitz, Ione Jones, Linda Kalof, Elinor K. Karlsson, Tammie King, Howard Litwak, Robbie A. McDonald, Luke John Murphy, Neil Pemberton, Daniel Promislow, Andrew Rowan, Peter W. Stahl, Jamshid J. Tehrani, Eric Tourigny, Clive D. L. Wynne, Eric G. Strauss, Greger Larson

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

VenueAnimals · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Victoria
FundersNational Institute on AgingArts and Humanities Research CouncilNational Institutes of HealthAnnenberg Foundation
KeywordsHumanityEnvironmental ethicsDomesticationCanisNatural (archaeology)Space (punctuation)The artsSociologyGeographySocial scienceEcologyPolitical scienceBiologyArchaeology

Abstract

fetched live from OpenAlex

), dogs have evolved alongside humans over millennia in a relationship that has transformed dogs and the environments in which humans and dogs have co-inhabited. The story of the dog is the story of recent humanity, in all its biological and cultural complexity. By exploring human-dog-environment interactions throughout time and space, it is possible not only to understand vital elements of global history, but also to critically assess our present-day relationship with the natural world, and to begin to mitigate future global challenges. In this paper, co-authored by researchers from across the natural and social sciences, arts and humanities, we argue that a dog-centric approach provides a new model for future academic enquiry and engagement with both the public and the global environmental agenda.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.186
GPT teacher head0.444
Teacher spread0.258 · 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