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Record W4382462483 · doi:10.1515/9780773554764

Obsession

2018· book· en· W4382462483 on OpenAlexaboutno aff

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2018
Typebook
Languageen
FieldArts and Humanities
TopicArt History and Market Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Sir William Van Horne (1843–1915), a gifted connoisseur most famously associated with the building of the Canadian Pacific Railway, amassed of one of the most extensive collections of Japanese ceramics in North America. Obsession is an illuminating account of the how and why behind his passion for studying and acquiring nearly 1,200 objects. Ron Graham assembles a profile of Van Horne's larger-than-life personality as well as essays about his place at the top of the art collectors in Montreal's Golden Square Mile and the afterlife of his collection following his death. Accompanying the texts are historical photographs and documents, a detailed catalogue of over three hundred individual pieces in the Royal Ontario Museum and the Montreal Museum of Fine Arts, and a selection of beautiful reproductions of Van Horne's personal notebooks and exquisite watercolours from the archives of the Art Gallery of Ontario. Published in conjunction with a major exhibition at the Gardiner Museum in Toronto and the Montreal Museum of Fine Arts, Obsession presents a remarkable collection in the context of the life and career of a nineteenth-century Canadian business giant.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.177
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.010
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0400.012

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 head0.022
GPT teacher head0.192
Teacher spread0.170 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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