Persuasive Evidence: Improving Customer Service through Evidence Based Librarianship
Bibliographic record
Abstract
Objective - To demonstrate how evidence based practice has contributed to informing decisions and resolving issues of concern in service delivery at Bond University Library. Methods - The paper critically analyses three evidence based research projects conducted at Bond University Library. Each project combined a range of research methods including surveys, literature reviews and the analysis of internal performance data to find solutions to problems in Library service delivery. The first research project investigated library opening hours and the feasibility of twenty-four hour opening. Another project researched questions about the management of a collection of feature films on DVD and video. The third project investigated issues surrounding the teaching of EndNote to undergraduate students. Results - Despite some deficiencies in the methodologies used, each evidence based research project had positive outcomes. One of the highlights and an essential feature of the process at Bond University Library was the involvement of stakeholders. The ability to build consensus and agree action plans with stakeholders was an important outcome of that process. Conclusion - Drawing on the experience of these research projects, the paper illustrates the benefits of evidence based information practice to stimulate innovation and improve library services. Librarians, like most professionals, need to continue to develop the skills and a culture to effectively carry out evidence based practice.
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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 | 0.366 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".