All together now!: integrating virtual reference in the academic library
Bibliographic record
Abstract
Although much as appeared in the literature regarding the initiation of virtual reference services, to date a case study discussing online reference service's integration into an academic library's current suite of reference services has not been written. At the University of Saskatchewan, the integration process forced the library to take a broader look at reference services as a whole and address several questions: What is reference? How should it be delivered? Who should provide it? On what reference service values are decisions being based? To facilitate the integration, it was necessary to identify the needs of our users and re-examine the core values of our reference service accordingly. This analysis resulted in system-wide changes to all the library's reference services. The paper concludes with a set of planning recommendations that will be useful for college and university libraries currently considering, or presently offering, a virtual reference or instant messaging service and aspiring to incorporate the service permanently into there suite of reference services.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".