From obsolescence to optimization: a predictive framework for managing print collection aging in academic libraries.
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| Title: | From obsolescence to optimization: a predictive framework for managing print collection aging in academic libraries. |
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| Authors: | Ma, Shuang1 (AUTHOR) 18101@tongji.edu.cn, He, Qingfang1 (AUTHOR) qfhe@tongji.edu.cn |
| Source: | Journal of Documentation. 2026, Vol. 82 Issue 4, p989-1002. 14p. |
| Subject Terms: | *Library circulation & loans, *Library administration, *Academic libraries, *Collection development in libraries, *Bibliometrics, *Machine learning, Collection management (Libraries), Shelving (Furniture) |
| Abstract: | Purpose: This study addresses the challenge of managing aging print collections in academic libraries by developing a data-driven framework that quantifies discipline-specific aging patterns and predicts future trends to inform dynamic shelving strategies. Design/methodology/approach: We extend Bernal's document aging model to library circulation data. Utilizing loan records (2001–2023) from 22 disciplines at a major university library, we fit a negative exponential decay model to quantify aging half-lives. A Gradient Boosting Regression Trees (GBRT) algorithm is then employed to forecast future aging parameters. These predictions inform a novel four-quadrant classification system and a weighted hierarchical prioritization model for shelving. Findings: The negative exponential model demonstrated robust goodness-of-fit (mean R2 = 0.75) across disciplines, validating its applicability to loan data. Significant cross-disciplinary heterogeneity was observed (mean half-life: 4.9 ± 0.7 years). The GBRT model achieved good prediction accuracy for initial loan rates (overall MAPE = 11.9%). The resulting framework categorizes disciplines into three actionable preservation tiers (Tier-1: open shelves; Tier-2: thematic displays; Tier-3: dense storage). Practical implications: The proposed framework offers library managers a replicable, data-driven tool to optimize physical space allocation, balance collection utility with preservation needs, and justify resource reconfiguration. Originality/value: This research makes three key contributions: (1) it theoretically extends document obsolescence analysis from citations to circulation data; (2) it methodologically innovates by integrating bibliometric modeling with machine learning for prediction; (3) it provides practitioners with a novel, predictive decision-making framework for evidence-based space management. [ABSTRACT FROM AUTHOR] |
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| Database: | Education Research Complete |
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