Open Access and Bottom Lines: Finance, Tech, and Libraries
Bibliographic record
Abstract
“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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".