MétaCan
Menu
Back to cohort

The Pan-American Nikkei Association: A Report on the Tenth and Eleventh Meetings

2002· article· en· W2176353276 on OpenAlexaboutno aff
James A. Hirabayashi, Akemi Kikumura-Yano

Bibliographic record

VenueAmerasia Journal · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsEleventhSocioeconomic statusImmigrationPolitical scienceEconomic growthSocioeconomicsGeographySociologyDemographyLaw

Abstract

fetched live from OpenAlex

The Pan-American Nikkei Association (PANA), founded on December 6, 1981 in Lima, Peru, is an organization of Nikkei who have established communities in Argentina, Bolivia, Brazil, Canada, Chile, Columbia, the U.S., Mexico, Paraguay, Peru, the Dominican Republic and Uruguay. Reminiscent of the early mutual aid associations among the early immigrants these countries, PANA fosters cooperative activities among the Nikkei as stated in its bylaws: to promote the international cooperation among its members in various corporate projects, resources and the exchange of experiences. According its bylaws, PANA supports research on Nikkei history, particularly the socioeconomic contributions and functions of Nikkei within each country, and promotes the dissemination of Japanese culture in general. With eyes on the future, PANA encourages youth participation and fosters exchange activities. Finally, PANA provides mutual assistance in case of emergencies or natural disasters.

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)0.003
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0200.006

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 head0.016
GPT teacher head0.258
Teacher spread0.242 · 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

Citations2
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueAmerasia JournalSame topicJapanese History and CultureFrench-language works237,207