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Record W4400471356 · doi:10.1515/9781802702071-026

Chapter 22. Seymour de Ricci and William Roberts: Recorders and Analysts of the Market

2024· book-chapter· en· W4400471356 on OpenAlexaboutno aff
Nigel Ramsay

Bibliographic record

VenueAmsterdam University Press eBooks · 2024
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomics

Abstract

fetched live from OpenAlex

the Late nineteenth and early twentieth centuries were a golden age for the freelance chroniclers and analysts of the European art world.There was even a living to be had from writing for the journals, reviews, magazines, and newspapers that then proliferated. 1 The two men who are the subject of the present chapter were particularly successful in making their mark in this world, but they may be said to represent merely the apex of a much larger realm of art critics, reviewers and historians.They owed their success to their ability to write in an informed way on a range of topics: each must have written more than one article every week, and each was able to offer a range of pertinent facts to back up his judgment.Seymour de Ricci had formidable talents that ranged widely and, when he chose, he went deeply into a variety of cultural fields: bibliography, especially historical, epigraphy, numismatics, art history (of almost all periods) and more. 2 Today he is best known for his Census of Medieval and Renaissance Manuscripts in the United States and Canada, published in three awkwardly sized volumes between 1935 and 1940.It does not quite meet all the standards set by later catalogues of medieval manuscripts such as those by the late Neil Ker, but for details of provenances of manuscripts and, quite simply, for the sheer scale of its scope-about 15,000 manuscripts, described in 2,343 pages-it is an astonishing achievement. 3Sadly he fell ill and died when barely sixty, when he had really

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.969
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.172
Teacher spread0.152 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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