Acquisition and Cataloguing Processes: Changes as a Result of Customer Value Discovery Research
Bibliographic record
Abstract
Objective: This study seeks to highlight the profound effect of Customer Value Discovery research on the internal business processes of two university libraries in the areas of cataloguing and acquisitions. 
 
 Methods: In this project, “Customer Discovery Workshops” with academic staff, students, and university stakeholders provided library managers and staff with information on what services and resources were of value to customers. The workshops also aimed to discover what features of existing library services and resources irritated the students, staff, and faculty. A student satisfaction survey assessed longer-term impact of library changes to students in one university.
 
 Results: The findings resulted in significant changes to collection development, acquisitions, and cataloguing processes. A number of value added services were introduced for the customer. The project also resulted in greater speed and efficiency in dealing with collection development, acquisitions, and cataloguing by the introduction of more technology-enhanced services. Overall customer satisfaction was improved during the project period. 
 
 Conclusions: The changes to services introduced as a result of customer feedback also improved relationships between librarians and their university community, through the introduction of a more proactive and supportive service.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".