Local Users, Consortial Providers: Seeking Points of Dissatisfaction with a Collaborative Virtual Reference Service
Bibliographic record
Abstract
Abstract Objective – Researchers at an academic library consortium examined whether the service model, staffing choices, and policies of its chat reference service were associated with user dissatisfaction, aiming to identify areas where the collaboration is successful and areas which could be improved. Methods – The researchers examined transcripts, metadata, and survey results from 473 chat interactions originating from 13 universities between June and December 2016. Transcripts were coded for user, operator, and question type; mismatches between the chat operator and user’s institutions, and reveals of such a mismatch; how busy the shift was; proximity to the end of a shift or service closure; and reveals of such aspects of scheduling. Chi-square tests and a binary logistic regression were performed to compare variables to user dissatisfaction. Results – There were no significant relationships between user dissatisfaction and user type, question type, institutional mismatch, busy shifts, chats initiated near the end of a shift or service closure time, or reveals about aspects of scheduling. However, revealing an institutional mismatch was correlated with user dissatisfaction. Operator type was also a significant variable; users expressed less dissatisfaction with graduate student staff hired by the consortium. Conclusions – The study largely reaffirmed the consortium’s service model, staffing practices, and policies. Users are not dissatisfied with the service received from chat operators at partner institutions, or by service provided by non-librarians. Current policies for scheduling, handling shift changes, and service closure are appropriate, but best practices related to disclosing institutional mismatches may need to be changed. This exercise demonstrates that institutions can trust the consortium with their local users’ needs, and underscores the need for periodic service review.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".