Evaluating Virtual Reference from the Users’ Perspective
Bibliographic record
Abstract
This article discusses the evaluation of virtual reference services from the user perspective. It is one outcome of a long-term research project, The Library Visit Study, which has been conducted in three phases at the University of Western Ontario for more than a decade. These studies have identified the need for, and essential components of, reference interviews and good reference behaviors. The third phase of this research focuses on the factors that make a difference to the users’ satisfaction with their virtual reference experience and whether these are the same or different from the ones we identified as important in face-to-face reference. An examination of user accounts of virtual reference transactions indicates that the reference interview has almost disappeared. Among the reasons identified for staff failure to conduct reference interviews in the virtual environment are: the nature of written vs. spoken interaction; the librarian's perceived need to respond quickly in this environment; and the rudimentary nature of the forms used in e-mail reference. The article includes a list of behaviors that users identified as either helpful or unhelpful and concludes with some implications of the research for good virtual reference service.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".