Navigating User Feedback Channels to Chart an Evidence Based Course for Library Redesign
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract
 
 Objectives – The objective of this project was to redesign library spaces based on the user feedback obtained from a broad complement of feedback channels. The over-arching goal of this project was to develop an evidence based approach to the redesign of library spaces. 
 
 Methods – Data from user-initiated and library-initiated feedback channels were collected and analyzed to determine priorities for library space changes. Online/onsite suggestions, a library onsite census survey, the LibQUAL+® survey, a whiteboard, ballot voting, and text voting were all used to gather input. A student advisory group was used as a sounding board for planned space changes before a final decision was made. 
 
 Results – Data produced by different feedback channels varied both in the number of suggestions generated as well as the changes requested. Composite data from all feedback channels resulted in a total of 687 suggestions identifying 17 different types of space changes. An onsite whiteboard, the LibQUAL+® survey, and library census proved the most prolific in producing suggestions. 
 
 Conclusion – Priorities for space changes were best determined through a composite of suggestions received from all feedback channels. The number of suggestions and requests received that were initiated by users was so small that it had to be supplemented with library-initiated feedback requests. The use of multiple feedback channels enhanced the number, variety, and scope of the suggestions that were received. Similar requests received through multiple feedback channels emphasized their importance to users. Focused follow-up feedback channels were effective in clarifying user suggestions for specific changes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.878 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it