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Record W2009395825 · doi:10.7202/1025000ar

Itinerant Jewish and Arabic Trading in the Dene’s North, 1916-1930

2014· article· en· W2009395825 on OpenAlexvenueaboutno aff
George Colpitts

Bibliographic record

VenueJournal of the Canadian Historical Association · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsCapitalismColonialismNegotiationModernization theoryFur tradeEconomic historyImmigrationHistoryJudaismCashEconomyPolitical scienceEconomicsLawArchaeology

Abstract

fetched live from OpenAlex

In late nineteenth century and especially in the interwar years, “free traders” took advantage of better transport systems to expand trade with Dene people in the Athabasca and Mackenzie Districts. Well versed in fur grading and supported by credit in the expanding industrializing fur industry in the south, “itinerant” peddlers worked independently and often controversially alongside larger capitalized fur companies such as the Hudson’s Bay Company. A large number of these newcomers were Jews. This article suggests that Jews and, to a lesser extent, Lebanese and other Arabic traders became critical in the modernization of the Canadian North. They helped create an itinerant trader-Dene “contact zone” where the mixed meaning of credit, cash, and goods transactions provided northern Aboriginal trappers the means to negotiate modernism on their own terms in the interwar years. However, by the late 1920s, the state, encouraged by larger capitalized companies, implemented policies to restrict and finally close down this contact zone. The history of itinerant trading, then, raises questions about the long-term history of capitalism and co-related economic neo-colonialism in the Canadian north and their impact on First Nations.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.413

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.004
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.195
Teacher spread0.185 · 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 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

Citations3
Published2014
Admission routes2
Has abstractyes

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Same venueJournal of the Canadian Historical AssociationSame topicCanadian Identity and HistoryFrench-language works237,207