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Record W1978350006 · doi:10.3163/1536-5050.100.2.007

Pay-per-view in interlibrary loan: a case study

2012· article· en· W1978350006 on OpenAlexaboutno aff
Heather L. Brown

Bibliographic record

VenueJournal of the Medical Library Association JMLA · 2012
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsnot available
Fundersnot available
KeywordsInterlibrary loanPurchasingPaymentQuarter (Canadian coin)BusinessLoanSample (material)Medical libraryLibrary scienceAdvertisingMarketingComputer scienceFinance

Abstract

fetched live from OpenAlex

QUESTION: Can purchasing articles from publishers be a cost-effective method of interlibrary loan (ILL) for libraries owing significant copyright royalties? SETTING: The University of Nebraska Medical Center's McGoogan Library of Medicine provides the case study. METHOD: Completed ILL requests that required copyright payment were identified for the first quarter of 2009. The cost of purchasing these articles from publishers was obtained from the publishers' websites and compared to the full ILL cost. A pilot period of purchasing articles from the publisher was then conducted. RESULTS: The first-quarter sample data showed that approximately $500.00 could have been saved if the articles were purchased from the publisher. The pilot period and continued purchasing practice have resulted in significant savings for the library. CONCLUSION: Purchasing articles directly from the publisher is a cost-effective method for libraries burdened with high copyright royalty payments.

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)0.008
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0070.003
Scholarly communication0.0040.004
Open science0.0020.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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 designQualitative
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

Citations23
Published2012
Admission routes1
Has abstractyes

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