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Record W4242940457 · doi:10.24908/iqurcp.8895

Arviq! The Northern Hunter’s Spiritual Connection to Animals and Community Presenter:

2018· article· en· W4242940457 on OpenAlexvenueaboutno aff
Chelsea Drent

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsArgument (complex analysis)SociologyValue (mathematics)MythologyEnvironmental ethicsAnthropologyHistoryPhilosophyClassics

Abstract

fetched live from OpenAlex

In Inuktituk, nuna means the land. It means the rocks, rivers, mountains and the forests. Nuna is everything, and all parts of the nuna have an inua, which means a living soul. There is a special, if not sacred relationship between members of northern communities and the nuna. However, these sacred relationships are all too often glossed over, if not forgotten. In the social sciences, author John Sorenson articulates a critical argument and evocative opinions about hunting in his article; Hunting is a Part of Human Nature (John Sorenson, “Hunting is a Part of Human Nature,” Culture of Prejudice, Arguments in Critical Social Science. Eds. Judith Blackwell, Murray Smith, John Sorenson, (Canada: Broadview Press, 2003).Sorenson demonstrates that hunting is an unnatural human activity which is linked to a cultural domination over animals. However, in these statements Sorenson neglects to consider the northern hunter in Inuit communities around the world. Cultural myths, social constructions and daily activities prove that hunting animals is a core value to how many Inuit peoples relate to each other and perceive themselves in the cosmos. This is a study that examines the relationship of people, land, animals and faith in order to understand the significance of hunting within Inuit cultures.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0170.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.003
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 head0.212
GPT teacher head0.468
Teacher spread0.255 · 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 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

Citations0
Published2018
Admission routes2
Has abstractyes

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