Analysis of Question Type Can Help Inform Chat Staffing Decisions
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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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 | 0.129 | 0.370 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".