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Record W2792815995 · doi:10.1111/mcn.12555

Transmitting Ainu traditional food knowledge from mothers to their daughters

2017· article· en· W2792815995 on OpenAlexfundno aff
Masami Iwasaki‐Goodman

Bibliographic record

VenueMaternal and Child Nutrition · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsIndigenousHarmony (color)Ethnic groupMainstreamDignitySociologyEthnologyAnthropologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Since 2004, research has been conducted in the Ainu Indigenous community of the Saru River Region of Biratori in Northern Japan examining traditional food use knowledge. The purpose was to improve the socio-cultural environment for the Ainu People by implementing interventions meant to reintroduce traditional Ainu food use, so that they can live with dignity and in harmony with non-Ainu people in the heterogeneous community where Japanese cultural values dominate. Ten years after the start of this research, a series of interviews was conducted with Ainu mothers and daughters active in the community to evaluate the result of the interventions because, in accordance with culturally established Ainu gender roles, the Ainu women prepare the Ainu dishes. The interviews indicated that the community of both Ainu and non-Ainu people shared traditional Ainu food as a communal food at community events organized by the Ainu members of the community. The people in the community now identify traditional Ainu dishes with Ainu names, indicating the establishment of culinary and linguistic boundaries between Ainu traditional food and mainstream Japanese food. This also signals that the Ainu People have begun to establish a basis for reconstructing their unique ethnic identity, once suppressed by the government's former assimilation policy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.423

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 studies0.0010.000
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 head0.025
GPT teacher head0.201
Teacher spread0.176 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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