Impact of Evidence Based Acquisitions (EBA) on Resource Sharing Activities of Canadian Research Libraries: How EBAs Impact the Ability to Share
Bibliographic record
Abstract
In Canada, university libraries play a key role in supplying research and scholarly items to college, public and special libraries’ patrons via resource sharing. At the same time Canadian university libraries have invested heavily in Evidence Based Acquisitions products, and ebooks in general. This paper presents the results of a survey of resource sharing specialists and licensing specialists at member libraries of the Canadian Association of Research Libraries. Recipients were surveyed for their work-informed impressions regarding resource sharing and licensing of ebooks; for evidence of declining resource sharing activity due to the rise in EBAs; and for evidence of communication, or non-communication, within libraries about the impact of e-books on resource sharing. The findings suggest that there is a causal link between the rise in EBAs and the decline in the ability of libraries to fulfill resource sharing requests. The findings also point to a probable lack of serious reflection by Canadian university libraries on the equity impacts of reducing resource sharing capacity through the acceptance of licensing terms that limit the ability to share ebooks within resource sharing networks.
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 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 | 0.018 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".