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Record W2337697277

Response: New Trees, New Medicines, New Wars: The Chickasaw Removal

2016· article· en· W2337697277 on OpenAlexvenueno aff
Linda Hogan

Bibliographic record

VenueCanadian review of comparative literature · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicArchaeology and Natural History
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)TreatyWildlifeState (computer science)Meaning (existential)HistoryEthnologyGeographyLawPolitical scienceEcologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

The Five Major Tribes of the Southeastern United States were removed to Indian Territory, Oklahoma. Indian Territory was a location created to contain all tribal peoples of the U. S. inside it, and then to have walls built around it so none could escape. It was not considered a state. The tribes, in need of assistance promised and denied by the Americans, signed a treaty with the Confederate States of the South. Still, they were removed from their rich lands to the drought-ridden grasslands which became called Oklahoma, our word meaning “Red People”, in 1927. The migration of the people was made difficult as they invaded the land of tribal peoples already present. They needed to learn new trees, had fewer water sources, and lacked knowledge of new medicinal plants. This paper is an account of that migration, even changes created for wildlife and birds, the new knowledge of plants, trees, and especially how new medicines were traded and learned. The stories are spoken by people. A historical memory, and also the stories of continued displacement by government actions and punishments against those who didn’t want to participate in wars soon undertaken. The presenter is from this nation, works there, and hears the stories people continue to tell. From an early age, she also had a mentor in medicinal knowledge, which she doesn’t use except as occasional recommendations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.627
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.326
Teacher spread0.298 · 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
GenreReview

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
Published2016
Admission routes1
Has abstractyes

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