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Record W3111676551 · doi:10.4324/9780367809317-27

Decolonizing bioarchaeology?

2020· book-chapter· en· W3111676551 on OpenAlexaboutno aff
Kisha Supernant

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsBioarchaeologyAnthropologyGeologyGeographySociology

Abstract

fetched live from OpenAlex

The fields of biological anthropology and bioarchaeology have changed significantly over the past 30 years. Many non-Indigenous biological anthropologists are working collaboratively with communities on projects where analysis is being done on Ancestors and sacred places, providing information for Indigenous peoples about their past at their request, as demonstrated by the chapters in this volume. However, there are still significant barriers to transformative and sustained change in biological anthropology and bioarchaeology, as researchers continue to analyze Ancestors without permission, museums repatriate Ancestors yet retain their belongings, and settler colonial frameworks continue to define much anthropological practice. In this chapter, I evaluate how far the field has come and explore where we need to go next. Using the Truth and Reconciliation Commission of Canada’s Calls to Action as a starting place, I explore how research in Canada and other settler colonial contexts can shift the power to communities and build models of decolonial, Indigenous-led practice, not just collaboration. Using recent examples of cases where analysis of Ancestors had occurred without collaboration, I argue that we need to push further into the foundational structures of the discipline to advocate for lasting change.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations3
Published2020
Admission routes1
Has abstractyes

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