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Record W4379780142 · doi:10.1353/jaie.2019.a798566

Maine Indigenous Education Left Behind: A Call for Anti-Racist Conviction as Political Will Toward Decolonization

2019· article· en· W4379780142 on OpenAlexaboutno aff
Rebecca Sockbeson

Bibliographic record

VenueJournal of American Indian Education · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipConvictionContext (archaeology)IndigenousPolitical scienceWaiverDecolonizationState (computer science)PoliticsPublic administrationLawSociologyHistory

Abstract

fetched live from OpenAlex

In 2001 the state of Maine passed the Wabanaki Studies Law, popularly referred to as LD 291 (per Maine Public Law 2001, Chapter 403, Title 20-A MRSA § 4706). Wabanaki, meaning “people of the dawn,” refers to the Penobscot, Passamaquoddy, Maliseet, Mi'kmaq, and Abenaki peoples residing in Maine and the Maritime Provinces of Canada. This precedent-setting curricular initiative requires K-12 educators to teach about the Indigenous people of Maine. The historical context of Wabanaki-colonizer relations and the current status of Wabanaki content delivery highlight challenges and opportunities in implementing the Wabanaki Studies Law. Intended to function as an educational policy working toward anti-racist education and decolonization of the Wabanaki, effective implementation requires reinstatement of the Wabanaki Studies Commission (WSC) with state and university backing; compulsory courses for preservice teachers; revised licensure requirements to ensure Maine teachers comply with the Wabanaki Studies Law and restoration of the full tuition waiver and room/board scholarship for Native American students at the University of Maine.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.362

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.278
Teacher spread0.268 · 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 designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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