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Record W7066388125

How the Ainu Became Jōmonese: Ainu Ancestry Through Japanese Eyes

2023· article· en· W7066388125 on OpenAlexaboutno aff

Bibliographic record

VenueScholarSpace (University of Hawaii at Manoa) · 2023
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPolityMetisMeiji periodUnpacking
DOInot available

Abstract

fetched live from OpenAlex

Despite only being declared Japan’s indigenous people in 2019, the Ainu within its borders have long been associated with the Jōmon, the archipelago’s earliest known inhabitants. Unpacking the labels of “Jōmon” and “Ainu” reveals the traits that caused these two peoples to be almost exclusively joined together in the first place, which in turn helped to influence the Ainu’s ability to claim indigeneity. Through this paper, I aim to show that the ways in which the Japanese have looked at the Ainu, from the time when Japanese chroniclers first started recording their state’s history to the present day, has contributed to the association of Hokkaidō’s indigenes with the Jōmon. Starting with the Emishi, the people(s) living on the outskirts of the Japanese polity were looked down on as barbaric or backwards in comparison to the refined Japanese. These views, while generally remaining static into the Meiji period, could shift depending on socio-political necessity and the acquisition of new ways of understanding their subjects, eventually culminating in a theory that the Neolithic residents of Japan and the Ainu were related. This theory was later confirmed by bioanthropological findings. Further reinforcing the bond are the ways in which the Ainu and their culture are currently presented to the Japanese public through museum exhibits and events.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.008
Scholarly communication0.0060.006
Open science0.0000.005
Research integrity0.0010.002
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 head0.034
GPT teacher head0.208
Teacher spread0.174 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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