Digging deeper into virtual reference transcripts
Bibliographic record
Abstract
Purpose – The purpose of the study is to identify the information needs of patrons in a large Canadian academic library system by analyzing the types of questions asked through the Library’s “Ask A Librarian” system. The results provide information on specific areas of competencies and training for staff providing virtual reference services. Design/methodology/approach – This article looks at virtual reference data collected between January and April 2012 from a large Canadian academic library and provides an analysis of the types of questions asked by library users. The researchers developed a detailed coding scheme for the analysis of question type and referrals made, and used the qualitative analysis software NVivo™ to code and analyze the data. Findings – The results of this analysis found that patrons often tap into synchronous online library help when they encounter challenges with online library resources. Specific areas of patron training to be developed were also identified. Finally, areas for staff training were uncovered which will help the library provide a consistent level of service to patrons. Originality/value – This is the first study in the library community to conduct a detailed analysis of the virtual reference transcripts from a large Canadian university using the NVivo™ content analysis software. The study developed and employed more detailed coding categories then has been used in previous studies to provide more information about the questions that patrons are unable to complete on their own. The study also captures detailed information pertaining to referrals.
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.
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 | 0.036 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".