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Record W7110550152

Open Access and Bottom Lines: Finance, Tech, and Libraries

2025· article· W7110550152 on OpenAlexaboutno aff

Bibliographic record

VenueSan José State University ScholarWorks (San Jose State University) · 2025
Typearticle
Language
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)Meaning (existential)NeutralityContext (archaeology)Information accessPower (physics)
DOInot available

Abstract

fetched live from OpenAlex

“It is often stated that libraries are not neutral, but when libraries rely on others to build connections on their behalf, the lack of neutrality deepens.” – Smith, 2025, p. 13 This presentation will combine philosophical and practical research findings to illustrate the impact that technological business interests have on access to information and cultural heritage preservation. Framed within the context of original research (2024, 2025), the presentation will begin with theoretical explorations of technological impacts, both local and global, on libraries. The second portion of the presentation will provide an institutional case study that exemplifies such theories. Both paywalled and open access will be discussed, with attention drawn to findings that illustrate through both theory and in practice that in a tech-centric world financial bottom lines shape cultural heritage. Smith, C. (2024). Lack of Collections as Data: Making Meaning Out of the Films We Cannot See. Canadian Journal of Information and Library Science, (47)3, 11-20. https://doi.org/10.5206/cjils-rcsib.v47i3.18988 Smith, C.F., ed. (2025). Platform Power and Libraries. (162) Sacramento, CA: Litwin Books (978-1-63400-156-4) https://litwinbooks.com/books/platform-power-and-libraries/ & https://spectrum.library.concordia.ca/995573/1/PPAL_final.pdf

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies, Scholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.007
Science and technology studies0.0050.003
Scholarly communication0.0070.040
Open science0.0050.006
Research integrity0.0000.001
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 head0.024
GPT teacher head0.276
Teacher spread0.252 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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