Let’s chat: the art of virtual reference instruction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study aims to evaluate the instances of information literacy instruction within the virtual reference system of a Canadian university library. Design/methodology/approach Coding and analysis of a sample of chat transcripts over the course of one academic year have been used. Findings The analysis indicated that over 50 per cent of virtual reference interactions do not lend themselves to information literacy instruction. An average of 23.6 per cent of interactions included information literacy instruction and the preferred methods of instruction were modelling and resource sharing. Originality/value While previous studies have focused on information literacy instruction provided in a virtual reference setting, this study aims to identify not only instances of information literacy but also to better understand the nature of chat queries by codifying instances of a transactional nature. The results could lead to improved best practices for chat reference, enhanced staff training and varied promotion and delivery of not just virtual reference services but of other library services as well. A portion of this research project, including partial results for the Fall semester, was presented at the LILAC Conference in Liverpool in April 2018.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it