Bibliographic record
Abstract
Purpose Using staff focus groups in the redevelopment of a library web site deploys their knowledge of user navigation issues and search strategies and addresses the unique needs of library staff. This paper seeks to describe the process of planning, recruiting, and conducting staff focus groups and provide a discussion of lessons learned. Design/methodology/approach A committee of professionals and non‐professionals from the University of Calgary Library conducted a series of five focus groups with library staff. The goals were to determine their content and service priorities for the redesigned library web site, and also to ensure that staff was included in the redesign process. Findings This paper makes recommendations for library staff focus group interviewing, including planning, formulating questions, recruitment, conducting sessions, and analysis and reporting. Practical implications Focus group interviews can be effectively conducted in‐house, with careful planning and adherence to established guidelines. Focus groups are a very useful method for gathering staff input for web site redesign or any other library‐planning project. Originality/value This paper will be useful to librarians interested in assessing staff needs and priorities through focus group interviews. The paper fills a void in the library literature regarding the use of library staff as both focus group leaders and participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.139 | 0.074 |
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".