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Record W4292327621 · doi:10.1017/aaq.2022.59

Archaeology and Social Justice in Native America

2022· article· en· W4292327621 on OpenAlexaff
Nicholas C. Laluk, Lindsay M. Montgomery, Rebecca Tsosie, Christine McCleave, Rose Miron, Stephanie Russo Carroll, Joseph Aguilar, Ashleigh Big Wolf Thompson, Peter Nelson, Jun Sunseri, Isabel Trujillo, GeorgeAnn M. DeAntoni, Greg Castro, Tsim D. Schneider

Bibliographic record

VenueAmerican Antiquity · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Toronto
FundersUniversity of California, Santa Cruz
KeywordsIndigenousPraxisSovereigntySociologyDecolonizationNexus (standard)Economic JusticeEnvironmental ethicsAnthropologyPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

Over the past 20 years, collaboration has become an essential aspect of archaeological practice in North America. In paying increased attention to the voices of descendant and local communities, archaeologists have become aware of the persistent injustices these often marginalized groups face. Building on growing calls for a responsive and engaged cultural heritage praxis, this forum article brings together a group of Native and non-Native scholars working at the nexus of history, ethnography, archaeology, and law in order to grapple with the role of archaeology in advancing social justice. Contributors to this article touch on a diverse range of critical issues facing Indigenous communities in the United States, including heritage law, decolonization, foodways, community-based participatory research, and pedagogy. Uniting these commentaries is a shared emphasis on research practices that promote Indigenous sovereignty and self-determination. In drawing these case studies together, we articulate a sovereignty-based model of social justice that facilitates Indigenous control over cultural heritage in ways that address their contemporary needs and goals.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0180.031
Scholarly communication0.0070.003
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.338
Teacher spread0.322 · 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
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

Citations50
Published2022
Admission routes1
Has abstractyes

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