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Record W2092592533 · doi:10.1080/01462679.2014.901200

70% and Climbing: E-Resources, Books, and Library Restructuring

2014· article· en· W2092592533 on OpenAlexaffabout
Laura Newton Miller, David J. Sharp, Wayne Jones

Bibliographic record

VenueCollection Management · 2014
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsCarleton University
Fundersnot available
KeywordsRestructuringClimbingLibrary scienceWorld Wide WebBusinessComputer sciencePolitical scienceHistoryArchaeologyFinance

Abstract

fetched live from OpenAlex

Within the past decade, academic libraries have seen a shift in purchasing from mostly print to mostly electronic. Although Carleton University Library (Ottawa, Canada) has experienced this shift, it had continued until recently to work within the confines of an organizational structure based on a print purchasing model. This paper will describe in detail the restructuring of the Library's collections and technical services departments to better meet growing electronic demands. Changes included dedicating more staff from print resources to e-resources, changing a librarian position to focus specifically on collections assessment, and shifting budgets to manage growing e-resources more efficiently.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0060.003
Scholarly communication0.0100.006
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.003

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 head0.007
GPT teacher head0.174
Teacher spread0.167 · 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 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

Citations7
Published2014
Admission routes2
Has abstractyes

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