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Record W4360783460 · doi:10.1007/978-981-19-9853-9_17

Edward S. Morse Japan Day by Day, 1877, 1878–1879, 1882–1883 (1917)

2023· book-chapter· en· W4360783460 on OpenAlexaff
Yuzo Ota

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicHistory of Science and Natural History
Canadian institutionsMcGill University
Fundersnot available
KeywordsMorse codeArt historyFolkloreThe artsHistoryClassicsArtEngineeringVisual artsLiterature

Abstract

fetched live from OpenAlex

Edward Sylvester Morse was born in Portland, Maine, U.S.A. in 1838. Although he worked mainly as a draftsman and did not go to university, Morse’s research—which he had been passionate about since childhood (e.g., collecting shells)—caught the attention of Louis Agassiz, a renowned naturalist, thus launching his career as a zoologist. A singularly gifted lecturer on popular science, Morse used the money from his winter lectures to pursue vigorous research at his own expense, and he gained sufficient fame as both a scholar and a proponent of evolutionism, to be elected a member of the National Academy of Sciences, the equivalent of the Academy of Sciences in 1876. Three visits to Japan between 1877 and 1883 marked a turning point in Morse’s life. The discovery of the Ōmori shell mound and a deep interest in Japanese ceramics and Japanese culture that was aroused during his visit to Japan led Morse to become a Japanologist rather than a zoologist. Morse wrote several excellent books on Japan, including Japanese Homes and Their Surroundings (1886) and Japan Day by Day (1917), and also gave numerous lectures on Japan, promoting understanding of Japan from a pro-Japanese standpoint. Morse’s collection of Japanese ceramics (in the Museum of Fine Arts Boston) and Japanese folklore (in the Peabody Museum, Salem) are both world-renowned. He died in 1925.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0370.025

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 head0.214
Teacher spread0.180 · 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; both teacher heads agree on what is shown here.

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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Same topicHistory of Science and Natural HistoryFrench-language works237,207