Walk-In Users and Their Access to Online Resources in Canadian Academic Libraries
Bibliographic record
Abstract
In the past, a member of the public could access an academic library’s collection simply by visiting the library in person and browsing the shelves. However, now that online resources are prevalent and represent the majority of collections budgets and current collections, public access has become more complicated. In Canadian academic libraries, licences negotiated for online resources generally allow on-site access for walk-in users; however access is not granted uniformly across libraries. The goal of this study was to understand whether members of the public are indeed able to access online resources in major Canadian university libraries, whether access to supporting tools was offered, how access is provided, and whether access is monitored or promoted. The study used an online survey that targeted librarians responsible for user services at Canadian Association of Research Libraries (CARL) member libraries. The survey results indicated that some level of free access to digital resources was provided to walk-in users at 90% of libraries for which a survey response was received. However, limitations in methods and modes of access and availability of supporting resources, such as software and printing, varied between the institutions. The study also found that most libraries did not actively promote or monitor non-affiliated user access.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".