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Record W2941696409

An Experimental Examination of Central Canadian Arctic Hunter-Gatherer Pottery and Soapstone Containers

2018· article· en· W2941696409 on OpenAlexaboutno aff
Liam Frink, Karen G. Harry

Bibliographic record

VenueDigital Scholarship - UNLV (University of Nevada Reno) · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPotteryHunter-gathererArchaeologyArcticGeographyThe arcticGeologyOceanography
DOInot available

Abstract

fetched live from OpenAlex

Our initial foray into the study of ceramic production in northern North America was stimulated by our profound respect at the ability of western Alaskan women to produce even “ugly” pottery under environmental conditions that can only be characterized as a potter’s nightmare. Here we turn our attention to ceramic containers found even further north, in the Central Canadian Arctic. Here, environmental constraints (fuel shortages and poor weather conditions) on ceramic production are similar to western Alaska, but even more extreme. In addition to facing problems in making the pottery, the people of the Central Canadian Arctic faced unique problems associated with their maintenance and use. Unlike the western coast, in this area an alternative cooking technology was present in the form of soapstone containers. We present the results of experiments undertaken to explore how these two materials compare in terms of engineering principles and performance characteristics. We then consider the broader social setting in which the technologies were employed, to examine how social and technological factors interacted to shape material culture.

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)0.006
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 score0.493
Threshold uncertainty score0.992

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0020.001
Open science0.0020.002
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 head0.030
GPT teacher head0.296
Teacher spread0.266 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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