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Record W2473657790 · doi:10.7202/1036083ar

English-Inuit hostilities at Cape Charles (Labrador) in 1767

2016· article· en· W2473657790 on OpenAlexafffundvenueabout
Hans Rollmann

Bibliographic record

VenueÉtudes/Inuit/Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of CanadaMemorial University of Newfoundland
KeywordsCulpabilityCapeCausationColonialismNarrativeHistoryGeographyEthnologyCriminologyLawPolitical scienceArchaeologySociologyArt

Abstract

fetched live from OpenAlex

In 1767, three English fishers associated with Nicholas Darby’s fishing and trading enterprise in southern Labrador were killed by Inuit. In response to this violence, a contingent of British soldiers from newly established York Fort pursued the alleged perpetrators, killed several, and captured others. Among the captives was an Inuk woman named Mikak as well as the future first Moravian Inuk convert, the youth Karpik. The hostilities of 1767 cannot be fully explored merely by following the narrative of colonial authorities and traders. If the Moravian records are consulted, the notorious murder near Cape Charles in 1767 appears to have had a more complex causation than has hitherto been suggested, one that may include some European culpability in these events. Instead of the unprovoked murder of three Europeans by Inuit during a robbery, it may have been a violent act of self-defence to prevent the theft of Inuit trading goods by English fishers. Whatever the original motivation for the killings of Nicholas Darby’s men may have been, the 1767 melee remains an important historical event in Labrador, which occurred during a decade that saw British legal and administrative changes reshape European relations with Inuit and a lasting Moravian presence on Labrador’s north coast established.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.269
Teacher spread0.239 · 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.

Study designQualitative
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
Published2016
Admission routes4
Has abstractyes

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