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Record W2033054688 · doi:10.5860/crl-280

Overlap between Humanities Faculty Citation and Library Monograph Collections, 2004–2009

2012· article· en· W2033054688 on OpenAlexaboutno aff
Jennifer Knievel, Charlene Kellsey

Bibliographic record

VenueCollege & Research Libraries · 2012
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsnot available
Fundersnot available
KeywordsInterlibrary loanCitationLibrary scienceQuarter (Canadian coin)Subject (documents)Library of Congress ClassificationCollection developmentOrder (exchange)Citation analysisInstitutionPlan (archaeology)Digital humanitiesPolitical scienceHistorySociologySocial scienceComputer scienceBusinessLibrary classification

Abstract

fetched live from OpenAlex

The authors wished to evaluate whether their collection housed the resources that their humanities faculty needed (and actually used) for their research, with the hope of providing additional illumination about general resource use by humanities scholars. This study asks not whether anyone used what was already owned, but instead whether the library owned what was needed. The answer to this question might have implications for storage or weeding decisions, approval plans for collections, and interlibrary loan. A citation analysis of 28 monographs published by their institution’s humanities faculty between 2004 and 2009 was used to assess how many of their cited sources were owned, how they were acquired (approval or firm order), their average age, and interdisciplinary usage as evidenced by LC classification. Subject areas assessed were History, Philosophy, Classics, and English. Findings include that one quarter of sources cited were over 25 years old, and that over the last fifteen years, the approval plan has provided more than three quarters of the sources cited that were owned.

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)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0710.100
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.116
GPT teacher head0.305
Teacher spread0.189 · 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.

Study designObservational
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

Citations31
Published2012
Admission routes1
Has abstractyes

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