Learning from Chatting: How Our Virtual Reference Questions Are Giving Us Answers
Bibliographic record
Abstract
Objective - This research compares two types of online reference services and attempts to determine whether the same sorts of questions are being asked; which questions are being asked most often; and whether patron and staff behaviour is consistent or different in the two types of online reference sessions. Patron satisfaction with the two types of online reference services is also examined. Methods - The researchers reviewed over 1400 online reference transcripts, including 744 from Docutek virtual reference (VR) transactions and 683 from MSN chat reference (IM) transactions. The questions were classified according to categories of reference questions based on recurring questions discovered during the review. Each transaction was also categorized as "informal" or "formal" based on patron language and behaviour, and general observations were made about the interactions between patrons and librarians. In addition, results from 223 user surveys were examined to determine patron satisfaction with online reference services and to determine which type of service patrons preferred. Results - The analysis suggests that patrons are using VR and IM services differently. In general, VR questions tend to be more research intensive and formal, while IM questions are less focused on academic research and informal. Library staff and patrons appear to alter their behaviour depending upon which online environment they are in. User surveys demonstrated that patrons are generally satisfied with either type of online reference assistance. Conclusion - Both types of online reference service are meeting the needs of patrons. They are being used for different purposes and in different ways, so it may be worthwhile for libraries to consider offering both VR and IM reference. The relationship building that appears to take place more naturally in IM interactions demonstrates the benefits of librarians being more approachable with patrons in order to provide a more meaningful 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 | 0.037 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".