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Record W2465249312 · doi:10.7202/1036082ar

Labrador Inuit and their arrow shafts

2016· article· en· W2465249312 on OpenAlexvenueaboutno aff
Greg Mitchell

Bibliographic record

VenueÉtudes/Inuit/Studies · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsAbies balsameaBalsamPeninsulaGeographyArchaeologyBaySoftwoodPinus <genus>ForestryBiology

Abstract

fetched live from OpenAlex

From the 16th to 18th centuries, Labrador Inuit seem to have valued softwoods (from coniferous tree boles) for the manufacture of arrows and darts used in hunting and warfare. Microscopic examination of Inuit arrow shafts from the Twin Island 3 site (EkBc-07) in Red Bay shows that balsam fir ( Abies balsamea ) was the preferred species for these purposes in the 16th century. Balsam fir is found in abundance in the inner bays of southern Labrador and was easily accessible to Inuit. However, archival sources indicate that by the 18th century Labrador Inuit desired another species of softwood for arrow and dart shafts, one that grew only on the island of Newfoundland. I propose that the sought-after species was one, or both, of the two pine species growing in central Newfoundland ( Pinus strobus or Pinus resinosa ) . Procurement of pine wood from Newfoundland would add another dimension to the established mobility and trading patterns of Inuit in southern Labrador. Conflicts with Europeans during the 16th through 18th centuries in the Strait of Belle Isle and the Petit Nord (on Newfoundland’s Great Northern Peninsula) may, in part, have been a result of the disruption in these travel and harvesting patterns. I suggest that iron products and wooden shallops (boats) from southern Labrador and northern Newfoundland were not the only “southern” commodities actively sought by Inuit during the early stages of European occupation; central Newfoundland’s pine wood was also important for manufacture of arrow shafts.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.232
Teacher spread0.206 · 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 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

Citations2
Published2016
Admission routes2
Has abstractyes

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