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Record W2340382264

American Indian Children and U.S. Indian Policy

2016· article· en· W2340382264 on OpenAlexaff
Angelique EagleWoman, G. William Rice

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsLakehead University
Fundersnot available
KeywordsPolitical scienceIndian countryEconomic JusticeCorporate governancePublic administrationEconomic growthDevelopment economicsLaw
DOInot available

Abstract

fetched live from OpenAlex

The accompanying article provides a broad perspective that contributes much to the areas of American Indian law, Family Law, and Tribal Law on the topic of American Indian children. By contextualizing the life experience of children within the accepted eras of U.S. Indian policy, the authors intend to provide insight into the contemporary experiences of American Indian children. Societal, health, and juvenile justice statistics are presented to highlight the quality of life issues impacting the next generations of tribal peoples in the United States.This is a significant work in addressing the often “invisibility” of American Indians and in particular children. As U.S. Indian policy has had a profound impact on tribal governance, the most vulnerable populations in tribal communities, children, have also been heavily impacted. The major U.S. Indian policies of warfare, assimilation through mandatory boarding school education, placing American Indian children into white adoptive homes, and addressing past policy negative consequences in contemporary federal initiatives are examined.As contemporary tribal governments seek to embrace educational reform and greater child protection measures, the federal funding available and interaction with federal agencies has become increasingly important. This article serves as foundational information piece tracing historical U.S. Indian policies impacting American Indian children to the present to inform policy makers, legal scholars, tribal leadership and others.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.256
Teacher spread0.252 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations6
Published2016
Admission routes1
Has abstractyes

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