Curriculum-Aligned Subject Assignment and Publication Coverage in a Forestry Library: Evidence from a Class S Bibliographic Subset

Statement of the Problem / Objectives. This study examined the analyzed Class S subset of a specialized academic forestry library in the Philippines. It determined how many distinct titles could be assigned to forestry-related subject groupings based on the Bachelor of Science in Forestry curriculum and described subject distribution, publication years, and Filipiniana/non-Filipiniana profile. Methods. Using a descriptive quantitative design and a curriculum-aligned collection analysis approach, the study analyzed 1,961 Class S record-level entries from a broader working print book collection of 5,364 volumes. Duplicate copies and repeated title entries were combined, resulting in 1,367 distinct titles. Titles were assigned only when the title and/or Library of Congress call number supported a clear match. Unassigned titles were coded by the clearest reason for non-assignment. Publication years for all distinct titles with usable year data were grouped into 5-year periods, while assigned titles were summarized separately by subject grouping using broader publication periods. Results. Of the 1,367 distinct titles, 720 (52.67%) were assigned, while 647 (47.33%) remained unassigned. Forest Resource Management and Silviculture accounted for 490 titles, or 68.06% of assigned titles. The subset was mostly non-Filipiniana but retained 388 Filipiniana titles. Publications were concentrated in older periods; only 12 assigned titles were dated 2010–2019. Conclusion. The analyzed subset provides partial, uneven, and mostly older curriculum-aligned support. Results can guide acquisition, updating, retention review, faculty consultation, and future assessment of other collection segments. Originality. The paper shows how a specialized academic library can convert legacy bibliographic records into curriculum-linked planning evidence through distinct-title analysis, conservative subject assignment, and coded non-assignment. It treats non-assignment as meaningful evidence rather than as missing data.