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Record W2321198821 · doi:10.7183/0002-7316.79.4.89

How Animals Create Human History: Relational Ecology and the Dorset–Polar Bear Connection

2015· article· en· W2321198821 on OpenAlexaff
Matthew Betts, Mari Hardenberg, Ian Stirling

Bibliographic record

VenueAmerican Antiquity · 2015
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of AlbertaWilfrid Laurier University
Fundersnot available
KeywordsCreaturesContext (archaeology)EcologyEthologyGeographyHistoryArchaeologyBiologyNatural (archaeology)

Abstract

fetched live from OpenAlex

Abstract Carvings that represent polar bears (Ursus maritimus) are commonly found in Dorset Paleo-Eskimo archaeological sites across the eastern Arctic. Relational ecology, combined with Amerindian perspectivism, provides an integrated framework within which to comprehensively assess the connections between Dorset and polar bears. By considering the representational aspects of the objects, we reveal an ethology of polar bears encoded within the carvings’ various forms. Reconstructing the experiences and perceptions of Dorset as they routinely interacted with these creatures, and placing these interactions in socioeconomic, environmental, and historical context, permits us to decode a symbolic ecology inherent in the effigies. To the Dorset, these carvings were simultaneously tools and mnemonics (symbols). As tools, they were used to directly access the predatory and spiritual abilities of bears or, more prosaically, to teach and remind of the variety of proper hunting techniques available for capturing seals. As symbols, however, they were far more powerful, signaling how Dorset people conceptualized themselves and their place in the universe. Symbolic of an ice-edge way of life, the effigies expose the role that this special relationship with polar bears played in the creation of Dorset histories and identities.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.021
Scholarly communication0.0040.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.078
GPT teacher head0.358
Teacher spread0.280 · 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

Citations45
Published2015
Admission routes1
Has abstractyes

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