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Record W4403666403 · doi:10.1139/as-2024-0066

A study of nanorriutit—polar bear hunting dogs in Avanersuaq

2024· article· en· W4403666403 on OpenAlexvenueno aff
Manumina Lund Jensen

Bibliographic record

VenueArctic Science · 2024
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
FundersAage og Johanne Louis-Hansens Fond
KeywordsPolarGeodesyGeographyPhysicsAstronomy

Abstract

fetched live from OpenAlex

Nanorriutit are specialized dogs integral to traditional nanoq (polar bear) hunting in Avanersuaq (Thule district, North Greenland). This practice, involving a deep relationship between the piniartoq (hunter), the nanoq, and the qimmeq (dog), is a crucial aspect of Inughuit cultural heritage in the region. However, the hunting culture associated with nanorriutit faces significant challenges due to the contemporary lifestyle, urbanization and impacts of climate change, which are deteriorating the traditional hunting grounds. This study aims to examine the evolving role of nanorriutit in polar bear hunting practices and the broader cultural context of Avanersuaq. Utilizing a qualitative approach, data were collected through fourteen semi-structured interviews with thirteen knowledge holders from the settlement of Savissivik and the town of Qaanaaq, conducted in Inuktun and kalaallisut. The findings illustrate both the enduring significance of nanorriutit , and the adaptations required in response to changing environmental conditions. This research contributes to the understanding of how the contemporary lifestyles and climate change is influencing Inughuit hunting practices and emphasizes the need to preserve this valuable cultural tradition amidst a rapidly transforming Arctic urbanization and environment.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2024
Admission routes1
Has abstractyes

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