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Record W4290546747 · doi:10.29173/cais1242

Toward information equity among academic libraries

2022· article· en· W4290546747 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2022
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsNorth Island College
Fundersnot available
KeywordsInterlibrary loanEquity (law)Academic libraryPremiseBusinessLibrary scienceIntermediaryHigher educationShared resourceLeasePolitical sciencePublic relationsWorld Wide WebMarketingComputer scienceFinanceLaw

Abstract

fetched live from OpenAlex

For two decades, publishers and vendors have used e-book licenses to back academic libraries into a corner. These rightsholders and intermediaries lease rather than sell content, and they dictate what constitutes permitted downstream usages. Libraries have historically used interlibrary loans to fill gaps in collections, but publishers and vendors unilaterally claim that interlibrary loans of entire e-books infringe on their exclusive rights. As a result, libraries at small and mid-sized colleges and universities are constrained to providing patrons access only to e-books that fall within the limits of modest collections budgets. Grounded on the premise that e-book interlibrary loans are needed to advance and protect information equity in higher education, this presentation invites interdisciplinary discussions and collaboration with respect to the future of resource sharing in academic libraries.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0050.062
Open science0.0040.005
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 head0.033
GPT teacher head0.235
Teacher spread0.202 · 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