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Record W2059405473 · doi:10.1108/ilds-06-2014-0029

CARL Libraries – a Canadian resource-sharing experience

2015· article· en· W2059405473 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInterlending & Document Supply · 2015
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInterlibrary loanShared resourceDatabase transactionConsistency (knowledge bases)Transactional leadershipResource (disambiguation)OriginalityData sharingTransaction dataValue (mathematics)Subject (documents)Computer scienceLibrary scienceKnowledge managementSociologyPublic relationsDatabasePolitical scienceSocial science

Abstract

fetched live from OpenAlex

Purpose – This study aims to map the current resource-sharing environment in Canada through the lens of its research libraries in general and the University of Alberta in particular. The findings present an interesting view of changing resource sharing patterns and trends. Design/methodology/approach – Interlibrary loan (ILL) transaction data were compiled from annual data reported to the Canadian Association of Research Libraries (CARL) and a case study of the University of Alberta is presented. Findings – The current trend shows declines in both borrowing and lending transactions. Research limitations/implications – Validity of the CARL ILL transactional data is subject to consistency in institutional reporting and accuracy of the data. The trends portrayed in the data are deemed realistic of the Canadian experience. Originality/value – This is an original study of CARL ILL transactional data, providing an aggregated view of 13 years of annual data, and an analysis of this data. It updates previous research and benchmarks current ILL patterns at CARL institutions.

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.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.621
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0030.005
Open science0.0020.001
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 head0.029
GPT teacher head0.242
Teacher spread0.214 · 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