Assessment of the Library Collection of the Central Luzon State University Library: Basis of the Collection Development Program
Bibliographic record
Abstract
Objective – The collection assessment project of the University Library is significant in determining whether the quantity of the collection meets the regulatory standard of the Commission on Higher Education (CHED) for academic libraries. This study specifically sought to find the level of library collection compliance in terms of major subject courses, to determine the curricular programs that are compliant with the standard or have a high rate of compliance, and to identify the curricular programs that should be prioritized in acquiring additional book titles. Methods – The assessment was conducted using an action research model of iterative reflection and improvement. It follows the four steps for carrying out the research: plan, act, observe, and reflect, as proposed by Davidoff and Van den Berg (1990). Furthermore, we employed CHED Memorandum Order (CMO) No. 22, Series of 2021, Section 4 (b.4-5) to analyze the collection's compliance based on its quantity. The data was presented using a table and percentage. Results – There are 32 undergraduate curricular programs offered at Central Luzon State University, which include 1,055 major subject courses. More than half of major subject courses (57.3%) on various curricular programs are non-compliant with CHED criteria, including 17.63% of major subject courses with zero titles copyrighted within the last five years. Findings also reveal that only 6 (18.75%) of the total programs were able to reach above 70% compliance with CHED standards, and there are 23 curricular programs with title gaps of 50% or higher that need to be prioritized in the acquisition of book titles. Conclusion – The library collection assessment technique is crucial for identifying gaps in the collection and determining areas where additional resources may be required. As the findings indicate that more than half of the major subject courses do not meet the requirements set by CHED, the librarians have been investigating ways to acquire additional academic sources to fill this gap. However, their current efforts are not yet enough to meet the requirements. A long-term plan for gradually building up the collection has been devised.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.046 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".