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Record W4399821347 · doi:10.58532/v3bilt3p4ch4

UNDERSTANDING INDIGENEITY—DEMOGRAPHY, CULTURE, AND LITERATURE

2023· book-chapter· en· W4399821347 on OpenAlexaboutno aff
Ms. Ancyea

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyDemographyAnthropologySociologyHistoryEthnologyGenealogy

Abstract

fetched live from OpenAlex

The term ‘indigenous’ implies belonging to a particular place, in its origins. It can be referred to people, culture, their art, cuisine, customs, habits, and lifestyle. Every country has an indigenous population which might have been erased, dominated, or marginalised by settler communities through wars, colonization, or migrations. Indigenous tribes exist in Australia, New Zealand, Africa, America, South America, Canada, Asia especially India. They are called by different labels in different countries. For instance, in the Indian constitution, they are referred to as Schedule Caste, Schedule Tribes, but otherwise called ‘Adivasis.’ In Australia they are the ‘Aboriginal people, in Canada they are ‘First Nation’ while in some other Asian countries they are ‘Janjati’ ‘Hunter-Gatherers’ or ‘Hill Tribes’.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.009
Science and technology studies0.0060.029
Scholarly communication0.0100.008
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.153
GPT teacher head0.325
Teacher spread0.172 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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