Dissonance between Perceptions and Use of Virtual Reference Methods
Bibliographic record
Abstract
This multimethod study investigates differences in question complexity and type between live chat, email, and texting by comparing findings from user interviews and virtual reference transcripts, with the goal of better understanding how different delivery methods can meet user needs in the context of an academic library. Findings reveal dissonance between perceptions and use of chat and email. Interviews suggest users consider chat to be for basic queries whereas transcripts coded using the READ Scale, a well-known reference assessment tool, show question complexity to be highest in chat. Our analysis also found statistically significant differences in the presence of reference interviews and instruction for chat, email, and texting. Rebranding chat more explicitly for intermediate and advanced queries may succeed in attracting users who consider chat only for basic queries, thus narrowing the gap between user perceptions and actual use.
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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 | 0.043 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".