Analysis of Question Type Can Help Inform Chat Staffing Decisions
Why this work is in the frame
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Bibliographic record
Abstract
A Review of:
 Meert-Williston, D., & Sandieson, R. (2019). Online Chat Reference: Question Type and the Implication for Staffing in a Large Academic Library. The Reference Librarian, 60(1), 51-61. http://www.tandfonline.com/doi/full/10.1080/02763877.2018.1515688
 Abstract
 Objective – Determine the type of online chat questions to help inform staffing decisions for chat reference service considering their library’s service mandate.
 Design – Content analysis of consortial online chat questions.
 Setting – Large academic library in Canada.
 Subjects – Analysis included 2,734 chat question transcripts.
 Methods – The authors analyzed chat question transcripts from patrons at the institution for the period of time from September 2013 to August 2014. The authors coded transcripts by question type using a coding tool created by the authors. For transcripts that fit more than one question type, the authors chose the most prominent type.
 Main Results – The authors coded the chat questions as follows: service (51%), reference (25%), citation (9%), technology (7%), and miscellaneous (8%). The majority of service questions were informational, followed by account related questions. Most of the reference chat questions were ready reference with only 16% (4% of the total number of chat questions) being in-depth. After removing miscellaneous questions, those that required a high level of expertise (in-depth reference, instructional, copyright, or citation) equaled 19%.
 Conclusion – At this institution, one in five chat questions needed a high level of expertise. Library assistants with sufficient expertise could effectively answer circulation and general reference questions. With training they could triage complex questions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.287 |
| Open science | 0.001 | 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 it