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Record W4366773340 · doi:10.3390/ani13081422

Mandan, Hidatsa, and Arikara Nation Perspectives on Rez Dogs on the Fort Berthold Reservation in North Dakota, U.S.A.

2023· article· en· W4366773340 on OpenAlexaff
Alexandra Cardona, Sloane M. Hawes, Jeannine Cull, Katherine Connolly, Kaleigh M. O’Reilly, Liana R. Moss, Sarah M. Bexell, Michael Yellow Bird, Kevin N. Morris

Bibliographic record

VenueAnimals · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsReservationIndigenousAnthropologyMedicineSociologyEthnologyPolitical scienceEcologyBiologyLaw

Abstract

fetched live from OpenAlex

The research on the relationships between free-roaming dogs, also referred to as reservation dogs or rez dogs, and Indigenous communities is extremely limited. This study aimed to document the cultural significance of rez dogs, challenges related to rez dogs, and community-specific solutions for rez dog issues affecting community health and safety from members of the Mandan, Hidatsa, and Arikara (MHA) Nation, also referred to as the Three Affiliated Tribes (TAT), who live on the Fort Berthold reservation in North Dakota, U.S.A. One hour semi-structured interviews with 14 community members of the MHA Nation were conducted in 2016. The interviews were analyzed via systematic and inductive coding using Gadamer's hermeneutical phenomenology. The primary intervention areas described by the participants included: culturally relevant information sharing, improved animal control policies and practices, and improved access to veterinary care and other animal services.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.111
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.004
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.346
Teacher spread0.295 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations5
Published2023
Admission routes1
Has abstractyes

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