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Record W2948968111 · doi:10.5525/gla.thesis.41207

Once upon a time in the Arctic: an analysis of Late Dorset metal exchange and interaction in the Eastern Arctic (AD 500-1300)

2019· dissertation· en· W2948968111 on OpenAlexaboutno aff
Patrick C. Jolicoeur

Bibliographic record

VenueEnlighten: Theses (The University of Glasgow) · 2019
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticThe arcticArchaeologyGeographyPhysical geographyOceanographyGeology

Abstract

fetched live from OpenAlex

Around AD 500 Palaeo-Inuit groups, known archaeologically as the Late Dorset, resettled parts of the Canadian Arctic and Greenland their ancestors had left uninhabited for nearly five hundred years. At this time, they started to use and exchange metal that derived from two native sources on opposite ends of the Eastern Arctic and potentially through exchange with the Greenlandic Norse. Despite metal being found in generally low quantities, the presence of it alone in many Late Dorset sites across the Arctic, some nearly one thousand kilometres away from potential sources, has led some researchers to suggest it is under-represented in current collections. This drastically hinders any attempt at understanding how much metal was being used, where it was being used, and why it was being used. Moreover, given its known wide distribution and constrained source regions, metal is a potentially important, measurable, and, arguably, unique indicator of the maximum extent of Late Dorset interaction networks. Fortunately, most Arctic sites have good organic preservation leaving the Late Dorset archaeological record rich in ivory, bone, and wood objects, such as harpoon heads and knife handles, that may have held metal blades. This thesis quantitatively and qualitatively assesses two key potential proxy indicators of metal use that has in the past been used successfully in Inuit contexts in order to better understand the extent, intensity, and nature of Late Dorset metal use and exchange. First, the analysis demonstrates that the thickness of blade slots of harpoon heads, side-, and end-hafted handles can be a reliable indicator for the raw material of the blade it once held. Once compared with lithic and metal blades to provide a baseline, the data show that blade slot sizes, particularly in the case of harpoon heads, become thinner during the Late Dorset period. In the case of one Late Dorset harpoon head type, metal was used more frequently than stone. Second, deposits left behind on those organic objects through contact with metal endblades were identified with a microscope. Despite the identification of these metal deposits being impacted by the object’s conservation and taphonomic history, no similar deposits were identified on any of the pre-Late Dorset material. This means that metal was being consistently and intensively exchanged over thousands of kilometers of Arctic landscape for a nearly eight-hundred-year period starting around AD 500. With these data in mind, the nature of this metal exchange can be examined with specific regards to the materiality of Late Dorset metal and the individual object itineraries that are created through the exchange process. The significance of metal within these continuous long-distance interaction networks enchained Dorset social relations through both time and space at a scale never before seen in the Eastern Arctic. It is along these same vectors of exchange that flowed the knowledge and ideas of what it meant to be Dorset.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.898

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.206
Teacher spread0.194 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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