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Record W2884127792 · doi:10.18002/rama.v13i1.5453

Tai-sabaki for the piano, tai-sabaki for the tatami – A tribute to Prof. em. David B. Waterhouse (1936-2017)

2018· article· en· W2884127792 on OpenAlexaboutno aff
Carl De Crée

Bibliographic record

VenueRevista de artes marciales asiáticas · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMartial Arts: Techniques, Psychology, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsTributeWoodcutPianoMagnum opusPolymathHonorClassicsArtSociologyArt historyLiterature

Abstract

fetched live from OpenAlex

David B. Waterhouse (1936-2017) was a Professor emeritus, Japanese studies scholar, and humanities polymath. Educated to concert pianist level, he graduated in Western Classics, Moral Sciences, and Oriental Studies from the University of Cambridge. It is there where during his freshman year he had attended for first time a live judo demonstration, and had decided to start his judo career. Professor Waterhouse would eventually join the University of Toronto, where he would spend the rest of his professional career as an educator and scholar. David aptly understood and taught judo as it was meant by its founder, i.e. as a form of pedagogy striving for both physical and intellectual development. Consequently, his academic judo classes at the University of Toronto’s Department of East Asian Studies attracted an enthusiastic crowd of students. Professor Waterhouse’s scholarly legacy is vast, showing a remarkable breadth in topics which he surveyed, investigated and mastered, but he was particularly proud of his magnum opus, i.e. a two-volume catalogue of woodcuts by Japanese artist Suzuki Harunobu published in 2013. The manuscript of his book on judo’s cultural and technical history, unfortunately, remains unfinished due to his untimely passing.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.077
GPT teacher head0.419
Teacher spread0.342 · 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
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
Published2018
Admission routes1
Has abstractyes

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