Out of the Question!...How We Are Using Our Students' Virtual Reference Questions to Add a Personal Touch to a Virtual World
Bibliographic record
Abstract
Objective - To investigate the types of questions students ask and the language they use in virtual reference. It is hoped that this examination will provide understanding of students’ needs and thus improve/enhance library services. Methods - Over 600 virtual reference transcripts were reviewed, analysed and categorised. This work was focused on three levels of analysis: broad categories based on the general type of question being asked, subcategories based on the specific question and the language that students used to ask their questions. Results - Students are primarily using the library’s virtual reference service for higher-level research assistance rather than using the tool to obtain quick answers to simple questions. The two most common types of questions involved staff providing detailed information or instruction on a topic. More specifically, the most frequently occurring type of question was related to finding journal articles on a given topic. Our analysis of the words students use to ask their questions confirmed that students and librarians often do not speak the same language. Conclusion - The results of our analysis of students’ needs and language can help us understand our users. This study demonstrated that our library can enhance services in five areas: online services, collections, relationships, staff skills, and the library as place.
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How this classification was reachedexpand
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.055 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".