Readers' Advisory: In the Readers’ Own Words: How User Content in the Catalog Can Enhance Readers’ Advisory Services
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
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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".