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Record W4316136112 · doi:10.1093/whq/whad009

A Legacy of Exploitation: Early Capitalism in the Red River Colony, 1763–1821. By Susan Dianne Brophy

2023· article· en· W4316136112 on OpenAlexaffabout
James Daschuk

Bibliographic record

VenueWestern Historical Quarterly · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCapitalismMarxist philosophyIndigenousSociologyColonialismProletariatDialecticPoliticsContext (archaeology)Power (physics)NarrativePolitical economyEnvironmental ethicsLawPolitical scienceHistoryEcologyArchaeology

Abstract

fetched live from OpenAlex

Such is the power of this book that is brings new life to the well-worn, even thread bare, narrative of the early days of the Red River colony. Using an unabashed dialectical materialist framework, Brophy’s careful analysis provides a powerful reinterpretation of the evolution of the colony through the emerging lens of settler colonial studies. Rather than the mechanistic outcomes of dependency theory and its inevitable “fatalistic metamorphosis into a wage labouring proletariat,” the author highlights the relative though uneven autonomy of Indigenous communities especially the Métis in their role as food providers to the fledgling and often floundering European settlement (p. 50). In uncovering the “social relations of land” (p. 178), Brophy goes beyond the pure economic coercion of earlier Marxist studies opening the way for an understanding of an ongoing conflict over land, arguing that draconian measures like the Indian Act, residential schools and other policies increasingly being described as genocidal are not driven simply by economic coercion in the settler colonial context but rather by social control.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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 designNot applicable
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
Published2023
Admission routes2
Has abstractyes

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