Stories They Borrow, Stories They Love: Reading Preferences and Circulation Patters as Foundations for Enhancing Fiction Services
Bibliographic record
Abstract
Abstract: Understanding the reading preferences of academic library users is essential for enhancing fiction services and encouraging greater leisure reading engagement. This study analyzes fiction circulation data over a span of seven academic years, drawing from the prior research conducted by Berenio and Calilung on the utilization of fiction books. Through a genre-based examination of borrowing trends, the study identifies high-demand categories such as romance, fantasy, and young adult fiction. These insights inform the development of a data-driven strategy for enriching the library’s fiction collection and readers’ advisory services. Findings demonstrate that aligning collection development with actual user behavior supports a more user-centered library experience, fosters student engagement, and reinforces the academic library’s role in holistic student development. The study concludes with actionable recommendations for collection management and service improvement based on empirical borrowing patterns.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".