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Record W2341623735 · doi:10.14288/1.0091598

Who are the Metis? : Olive Dickason and the emergence of a Metis historiography in the 1970s and 1980s

2009· article· en· W2341623735 on OpenAlexaboutno aff
Margaret Inoue

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicPhilippine History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsHistoriographyHistoryPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

This paper examines contribution that historian, Olive Dickason, has made to Métis historiography. In doing so, it also examines the ways in which the origins of Métis culture and identity were represented in historical studies of the 1970s and 1980s. The question of who the Métis are is being raised publicly as a result of the recent Powley court decision. However, the question of Métis origins is not a new one; the 1970s and 1980s saw the first major period examining the history of the Métis. It was during this same period that Dickason completed her studies and produced her early work on North American contact. When determining Métis origins, the convention has been to focus on prairie peoples of French Catholic descent. Some works, though, examine Métis people outside of the prairies, pointing toward a more diverse understanding of Métis peoples and origins. While Olive Dickason follows many of the conventional patterns, she expands the literature by revealing a fluid development of Métis identity from the point of contact. In order to be able to speak of the Métis peoples and determine who has aboriginal rights as a Métis person, identity needs to be determined not only from external historical sources, but also from internal sources within the Métis communities.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.939
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0100.019
Scholarly communication0.0060.006
Open science0.0000.003
Research integrity0.0020.004
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 head0.007
GPT teacher head0.184
Teacher spread0.176 · 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
Published2009
Admission routes1
Has abstractyes

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Same venuecIRcle (University of British Columbia)Same topicPhilippine History and CultureFrench-language works237,207