Pay (No) Attention to the Man Behind the Curtain: The Effects of Revealing Institutional Affiliation in a Consortial Chat Service
Bibliographic record
Abstract
This study aims to understand how users within a library consortium perceive chat service provided by staff members who are unaffiliated with the user’s home library. The researchers examined 293 chat interactions from Ask a Librarian, a consortial virtual reference service provided to university libraries across Ontario, Canada. Chi-square tests of independence were performed to explore the relationship between user dissatisfaction and instances where the chat operator revealed a mismatch in institutional affiliation between the operator and the user. Moderating variables in the relationship were investigated, including user type, question type, and operator behaviors like transferring the chat, making a referral, revealing a lack of expertise, and saying no to the patron. The researchers found that when an operator revealed that they work at a different institution than the user, patrons are more likely to be dissatisfied if they are graduate students, if their question is research-related, if the operator does not offer to transfer the chat, and if the operator does not state that they lack expertise on the chat topic. These findings suggest that chat operators should be mindful of context and relationships when revealing information about their affiliation. Users may perceive operators from other institutions as lacking knowledge about their local library, or they may be confused or alienated when receiving “behind the scenes” information about staffing that they perceive as unnecessary. The researchers recommend emphasizing and strengthening the user’s relationship with their home library and local library staff.
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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 | 0.009 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".