Readers' Advisory: In the Readers’ Own Words: How User Content in the Catalog Can Enhance Readers’ Advisory Services
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
It’s always challenging and exciting to find topics for the readers’ advisory column, and professionals willing to write for them! I’ve been so thankful to the many professionals who have so generously given their time and shared their expertise for this column. From lessons learned, case studies and differing opinions on RA and its future, it is amazing how various and rich this area of librarianship is—and how rewarding and frustrating! In an effort to continue to provide a broad spectrum of thoughts and ideas, I asked Dr. Louise Spiteri of Dalhousie University to write for this issue. Spiteri recently completed two stages of research examining subject headings and user-generated content and how these connect with RA access points. Jen Pecoskie was Spiteri’s research partner in both studies.—Editor
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.008 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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