OPACs, Users, and Readers’ Advisory: Exploring the Implication of User-Generated Content for Readers’ Advisory in Canadian Public Libraries
Bibliographic record
Abstract
An analysis of user-generated content (UGC) of 22 adult fiction titles in 43 Canadian public libraries that use BiblioCommons, SirsiDynix, and Encore was conducted to examine the contribution of UGC on readers’ advisory services. Findings indicate that UGC provides insight into the affect, subject, and protagonists of a work.Nous avons procédé à une analyse de contenus générés par les utilisateurs sur 22 titres de fiction pour adultes dans 43 bibliothèques publiques canadiennes utilisant BiblioCommons, SirsiDynix et Encore, afin d’examiner la contribution du contenu généré par les utilisateurs aux services d’avis aux lecteurs. Les résultats indiquent que les contenus générés par les utilisateurs donnent un aperçu sur le sujet, les protagonistes et les affects d'une oeuvre.
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.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".