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Record W4380271271 · doi:10.1515/9780228012498

Heirs of an Ambivalent Empire

2022· book· en· W4380271271 on OpenAlexaboutno aff
Scott Berthelette

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2022
Typebook
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsEmpireAmbivalenceArtHistoryAncient historyPsychologyPsychoanalysis

Abstract

fetched live from OpenAlex

The fur trade was the heart of the French empire in early North America. The French-Canadian (Canadien) men who traversed the vast hinterlands of the Hudson Bay watershed, trading for furs from Indigenous trappers and hunters, were its cornerstone. Though the Canadiens worked for French colonial authorities, they were not unwavering agents of imperial power. Increasingly they found themselves between two worlds as they built relationships with Indigenous communities, sometimes joining them through adoption or marriage, raising families of their own. The result was an ambivalent empire that grew in fits and starts. It was guided by imperfect information, built upon a contested Indigenous borderland, fragmented by local interests, and periodically neglected by government administrators. Heirs of an Ambivalent Empire explores the lives of the Canadiens who used family and kinship ties to navigate between sovereign Indigenous nations and the French colonial government from the early 1660s to the 1780s. Acting as cultural intermediaries, the Canadiens made it possible for France to extend its presence into northwest North America. Over time, however, their uncertain relationships with the French colonial state splintered imperial authority, leading to an outcome that few could have foreseen – the emergence of a new Indigenous culture, language, people, and nation: the Métis.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.434
Threshold uncertainty score0.872

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0170.039
Scholarly communication0.0080.004
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0070.002

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 head0.016
GPT teacher head0.216
Teacher spread0.200 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

Explore more

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