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Record W2074569594 · doi:10.1163/156853011x545510

Human-Sled Dog Relations: What Can We Learn from the Stories and Experiences of Mushers?

2011· article· en· W2074569594 on OpenAlex
Gail J. Kuhl

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSociety and Animals · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsLakehead University
Fundersnot available
KeywordsNarrativeGeneral partnershipQuality (philosophy)PsychologyQualitative researchSociologyEpistemologyPolitical scienceSocial scienceLinguistics

Abstract

fetched live from OpenAlex

Abstract In this qualitative study, the elements and quality of musher-sled dog relationships were investigated. In-depth interviews with a narrative design were conducted with eight mushers from northern Minnesota and northwestern Ontario. The mushers were asked to contribute ideas by sharing stories and experiences of working with dogs, as well as art or photographs. While all the participants had their own ideas about musher-sled dog relationships, six themes emerged. The mushers stated the importance of getting to know the dogs, their respect for their sled dogs’ abilities, the idea of a two-way communication that takes place, the importance of trust, the notion of partnership, and what can be learned through working with sled dogs. This study supports other research suggesting that humans and animals engage in interspecies relationships and that these can be quality relationships with multiple elements. The importance of researching and teaching about dogs as subjects is discussed, as well as the significance of humans having direct experience with other animals.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.232

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)0.0000.000

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 head0.044
GPT teacher head0.326
Teacher spread0.282 · 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