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Conservation Canines

2019· book-chapter· en· W3001679054 on OpenAlexaboutno aff
Renée D’Souza, Alice J. Hovorka, Lee Niel

Bibliographic record

VenueOxford University Press eBooks · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)EthogramAgency (philosophy)WelfareParticipant observationAnimal welfareValue (mathematics)Social psychologyPsychologyEnvironmental ethicsPublic relationsSociologyPolitical scienceSocial scienceEngineeringEcologyLaw

Abstract

fetched live from OpenAlex

For centuries, dogs have played a key role in the lives of humans both as companions as well as working animals. In recent years, the value of dogs in environmental work has been documented in the literature—namely their ability to detect targets more efficiently than humans and equipment. However, the environmental work dogs perform in Canada has been largely understudied in terms of both the specific tasks they are responsible for, as well as their welfare within these roles. This chapter addresses those gaps through an exploration of whether conservation canines could be an example of a humane job—one that is good for people, animals, and the environment. To do so this chapter explores tangible and moral issues related to dogs’ enjoyment of and suffering within conservation work, highlighting the complexity of dogs’ work-lives related to issues of freedom and consent. Findings are presented from two main case studies: Alberta and Ontario. An ethogram was used to assess dog welfare, while semi-structured interviews and participant observations revealed further insights into dogs’ work and work-lives. Ultimately, this chapter offers a discussion regarding how the study’s findings might inform assessment of humane jobs and work-lives, offering enjoyment, control, agency, respect, and recognition for dogs in this sector and for possibilities of fostering interspecies solidarity in other areas.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.976
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 studies0.0010.000
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 head0.039
GPT teacher head0.255
Teacher spread0.216 · 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.

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".

Quick stats

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueOxford University Press eBooksSame topicGeographies of human-animal interactionsFrench-language works237,207