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Record W2957730870 · doi:10.7202/1061437ar

Covering Bones: The Archaeology of Respect on the Kazan River, Nunavut

2019· article· en· W2957730870 on OpenAlexaffvenueabout
T. Max Friesen, Andrew Stewart

Bibliographic record

VenueÉtudes/Inuit/Studies · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsArchaeologyGeographySoulHuman boneEthnographyZooarchaeologyDisturbance (geology)HistoryGeologyPaleontology

Abstract

fetched live from OpenAlex

Complex relationships between people and animals define life in the northern past. For Inuit these relationships are manifested in many ways, particularly in practices that are often described as showing respect for animals, thus promoting stable relations between animal and human societies. Frustratingly, many of these activities, which are so prominent in the ethnographic record, have few archaeological correlates. Here, we examine one important practice with a relatively high level of archaeological visibility: the concealment of caribou bones under stones and in other inaccessible areas, which thereby protect them from dogs and other disturbances that could offend the caribou’sinua(spirit, soul). We examine this phenomenon at several important caribou crossings and elsewhere at inland Inuit archaeological sites on the Kazan River, southern Nunavut, where we have conducted extensive surveys. This research was performed in collaboration with Baker Lake community members who have direct knowledge of these localities, including aspects of bone disposal. Together, these studies reveal a cultural landscape in which the human–caribou relationship is omnipresent, not just in terms of features relating to hunting and storage, but also with regard to the spiritual connection between these two interdependent categories of being.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.766

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.005
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.395
Teacher spread0.312 · 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 designObservational
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
Published2019
Admission routes3
Has abstractyes

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