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Record W2134843083 · doi:10.7939/r38x92

"Criteria for Selecting Electronic Books in an Academic Library: Will we ever need to buy paper again?"

2000· article· en· W2134843083 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversity of Alberta Library · 2000
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)Subject (documents)PreferenceComputer scienceFactor (programming language)Work (physics)Library scienceElectronic bookSociologyArtificial intelligenceEngineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Based on the assumption that all books will soon be available in both electronic and paper formats, selections librarians will soon be faced with a format decision for each title they purchase. The work of Summerfield, Mandel and Kantor at Columbia University has given us some early information about the ways in which academics use electronic materials. They identified length of use (read little vs read much) as being a defining factor in a scholar's preference for electronic or paper format 1 . With this factor in mind, qualitative research was undertaken at the University of Alberta to determine whether or not there are general or specific criteria which would help selectors determine which books would be read little or read much by faculty. Faculty members in a variety of subject areas were introduced to netLibrary or ENGnetBASE publications. They were then asked a series of questions about their potential use of the materials. The explanations for their choices were noted and revealed patterns of factors affecting their choices. These patterns form some preliminary criteria for selectors who need to choose between e-books and paper books.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.016
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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