Analyzing Public Library Service Interactions to Improve Public Library Customer Service and Technology Systems
Bibliographic record
Abstract
Abstract Objective – To explore the types and nature of assistance library customers are asking library staff for in a large Canadian urban public library system. Methods – A qualitative study employing transaction logging combined with embedded observation occurred for three-day sample periods at a selection of nine branches over the course of eight months. Staff recorded questions and interactions at service desks (in person, by phone, and electronically), as well as questions received during scheduled and non-scheduled provision of mobile reference service. In addition to recording interaction details and interaction medium, staff members were also asked to indicate briefly the process or resources used to resolve the interaction. Survey data were entered and coded through thematic analysis. Results – The survey collected 6,099 interactions between staff and library customers. Of those 6,099 interactions, 1,920 (31.48%) were coded as pertaining to technology help. Further analysis revealed significant library customer need for help with Internet workstations and printing. Conclusions – Technology help is a core customer need for Edmonton Public Library, with requests varying in complexity and sometimes resolved with instruction. The library’s Internet workstations and printing system presented critical usability challenges that drove technology help requests.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".