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Record W7093569784

Review of <i> Grave Injustice: The American Indian Repatriation Movement and NAGPRA</i> By Kathleen S. Fine-Dare

2004· article· W7093569784 on OpenAlexaboutno aff

Bibliographic record

VenueInsecta mundi · 2004
Typearticle
Language
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRepatriationPower (physics)Native americanState (computer science)Movement (music)
DOInot available

Abstract

fetched live from OpenAlex

Fine-Dare tells the story of the American Indian movement to recover human remains and cultural objects taken from them by non-Indians for the purposes of study, display, and profit from the viewpoint of an anthropologist supportive of Indian issues who wants her profession used in a more positive way regarding Native peoples. To her, the Native American Graves Protection and Repatriation Act (NAGPRA) is a means for understanding boarder historical currents invol ving the treatment of Indians, whether bad, good, or indifferent. She argues that relating this history is vital for fathoming the complexities of repatriation that began during the 1880s and continues to present times in both the United States and Canada. Fine-Dare also traces the development of federal and state laws related to Indian burial rights and repatriation. In discussing the impetuses behind non-Indian motives for the sacrilege, she examines the connection between the views of John Locke, Calvinists, Thomas Jefferson, Franz Boas, and others about the alleged superiority of Western society, along with their declarations proclaiming Native peoples' inferiority, and the rise of scientific enquiry and museums. Out of these intellectual processes grew a moral climate, supported by a power relationship and public sentiments, that sanctioned the practices of collecting, studying, and exhibiting Native remains and cultural items in museums through heinous means, including the decapitations of fallen warriors, grave looting, and theft.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

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
Published2004
Admission routes1
Has abstractyes

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