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Record W2271818307 · doi:10.26443/fo.v13i.251

Discovering a ‘Hidden’ Collection: Early Printed Books and Old Master Prints in the McGill Library Collected by T.W. Mussen

2013· article· fr· W2271818307 on OpenAlexfundvenueaboutno aff
Svetlana Kochkina

Bibliographic record

VenueFontanus · 2013
Typearticle
Languagefr
FieldSocial Sciences
TopicHistorical and Literary Studies
Canadian institutionsnot available
FundersMcGill University
KeywordsLibrary scienceArtHumanitiesCollection developmentSubject (documents)Art historyComputer science

Abstract

fetched live from OpenAlex

Thomas W. Mussen (1832–1901) was the rector of the Anglican Church at West Farnham, Québec from 1859 to 1901, and bequeathed his collection of old master prints and early printed books to McGill University. This article describes a special project undertaken by the author in order to reconstruct the collection that has long remained hidden from the research community. This article analyses the book collection under language (5 categories), place of publication (3), date of publication (7), and subject (8) and demonstrates the uniqueness of the collection by comparing it to the holdings of other Canadian libraries.ResuméThomas W. Mussen (1832–1901) était le recteur de l’église anglicane à West Farnham, Québec de 1859 à 1901 qui a légué sa collection d’estampes et livres rares à l’Université McGill. Cet article décrit le projet spécial entrepris par l’auteur dans le but de reconstruire de sa collection longtemps cachée de la communauté de recherche. Cet article analyse la collection de livres en fonction de la langue (5 catégories), du lieu de publication (3), de la date de publication (7), et des sujets (8) et prouve le caractère unique de sa collection en la comparant aux fonds des autres bibliothèques canadiennes.

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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.432

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.010
Science and technology studies0.0190.016
Scholarly communication0.0120.005
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.017
GPT teacher head0.213
Teacher spread0.197 · 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
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
Published2013
Admission routes3
Has abstractyes

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