An evidence based methodology to facilitate public library non-fiction collection development - Kelly, Matthew (ANZSOG, 2015)
Objective – This research was designed as a pilot study to test a methodology for subject based collection analysis for public libraries. Methods – WorldCat collection data from eight Australian public libraries was extracted using the Collection Evaluation application. The data was aggregated and filtered to assess how the sample’s titles could be compared against the OCLC Conspectus subject categories. A hierarchy of emphasis emerged and this was divided into tiers ranging from <0.1% of the sample to >1% of the sample. These tiers were further analysed to quantify their representativeness against both the sample’s titles and the subject categories taken as a whole. The interpretive aspect of the study sought to understand the types of knowledge embedded in the tiers and was underpinned by hermeneutic phenomenology. Results – The study revealed that there was a marked tendency for a small percentage of subject categories to constitute a large proportion of the potential topicality that might have been represented in these types of collections. The study also found that distribution of the aggregated collection conformed to a Power Law distribution (80/20) so that approximately 80% of the collection was represented by 20% of the subject categories. The study also found that there were significant commonalities in the types of subject categories that were found in the designated tiers and that it may be possible to develop ontologies that correspond to the collection tiers. Conclusions – The evidence-based methodology developed in this pilot study has the potential for further development to help to improve the practice of collection development. The introduction of the concept of the epistemic role played by collection tiers is a promising aid to inform our understanding of knowledge organization for public libraries. The research shows a way forward to help to link subjective decision making with a scientifically based approach to managing knowledge resources.