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Record W2782805317 · doi:10.1177/0306312717751863

‘We’ve been here for 2,000 years’: White settlers, Native American DNA and the phenomenon of indigenization

2018· article· en· W2782805317 on OpenAlexaffabout
Darryl Leroux

Bibliographic record

VenueSocial Studies of Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsIndigenousKinshipIndigenizationWhite (mutation)GenealogyPhenomenonDiversity (politics)CitizenshipRace (biology)SociologyPolitical scienceGender studiesEthnologyEnvironmental ethicsAnthropologyBiologyHistoryGeneticsLawPoliticsEcologyGeneEpistemology

Abstract

fetched live from OpenAlex

Relying on a populace well-educated in family history based in ancestral genealogy, a robust national genomics sector has developed in Québec over the past decade-and-a-half. The same period roughly coincides with a fourfold increase in the number of individuals and organizations in the region self-identifying with a mixed-race form of indigeneity that is counter to existing Indigenous understandings of kinship and citizenship. This paper examines how recent efforts by genetic scientists, working on a multi-year research project on the 'diversity' of the Québec gene pool, intervene in complex settler-Indigenous relations by redefining indigeneity according to the logics of 'Native American DNA'. Specifically, I demonstrate how genetic scientists mobilize genes associated with Indigenous peoples in ways that support regional efforts to govern settler-Indigenous relations in favour of otherwise white settler claims to Indigenous lands.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.806

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.020
Scholarly communication0.0040.002
Open science0.0010.001
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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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.

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

Citations51
Published2018
Admission routes2
Has abstractyes

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