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An Analysis of Motivations, Methods and Metrics. Weeding the Collection:. Patron Satisfaction Cycle. How do we improve the search/acquisition practice? Acquire resources based on patron borrowing practice Remove resources that do not experience activity Repeat. ACRL Standard.
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An Analysis of Motivations, Methods and Metrics Weeding the Collection:
Patron Satisfaction Cycle • How do we improve the search/acquisition practice? • Acquire resources based on patron borrowing practice • Remove resources that do not experience activity • Repeat
ACRL Standard • “collection currency and vitality should be maintained through judicious weeding.” Discard materials: • “which have outlived their usefulness” • for which “a clear purpose is not evident.” What do these terms really mean? Is the lack of rigid guidelines a consequence given the set of conflicts, challenges, and varied motivations weeding presents? American Library Association, Association of College and Research Libraries, College Library Standards Committee, “Standards for College Libraries, 1995,” College and Research Library News (April 1995): 247. Print.
Historical Motivations • Against Weeding (or at least discouraged from doing so) • Collections should be kept intact • Budget / Comparisons • Policy • Politics • Librarian’s time or motivation
Historical Motivations • For Weeding • Space • Increased Circulation • Ease of Use / Patron Perception • “An information retrieval system will tend not to be used whenever it is more painful and troublesome for a customer to have information than for him not to have it.”
Methodologies • Qualitative (judgment-based decisions by librarian) • Outcomes vary with decision maker • Time consuming • Future use is less predictive • Opportunities exist to “change the game plan” (e.g., pressure, emotion) • Leads to increasing variability • Can become incapacitating
Methodologies • Quantitative (objective-based decisions by algorithm) • Uses objective metrics such ascirculation rates, citation frequency, in-library use, age, search statistics • Ideal for computer application, can produce results in minutes • Reveals current and can reasonably predict future use • Does not vary unless programmed to do so
Objective-based Weeding - Use • “Shelf-time periods” - Least use over time • Time period & Cutoff percentage • Strong indicator of patron preference • Strong predictor of future use
1 - Age and Usage of New Titles • Age of title is low predictor – in general! • Kent study – first time use • Year one = 26% of new titles used • Year two = 17% • Year three = 6% • In total, 40% of new titles never circulated over seven years. • Is there consideration for incubation, embargo on new titles? • Remove all titles that have not circulated over last 8, 9, 10 years?
2 - The Core Collection • Entire collection – Unused collection = Core Collection • Example: • 99% of patrons checked out 25% of the entire collection. 25% is core collection. • If the remaining 75% were removed, patrons are unaffected, but collection size drastically reduced. • Usage calculations go from 25% to near 100%
3 - Discipline Differences • Volatility between collections – Computer Science versus English literature • Volatility within collection – Alternative energy, spring design, thermodynamics • “Core collections” are discreet sets within subject categories (QA76+TK5105, TK300’s with TK9000’s?)
4 - “Needs versus Wants” • Needs – what the librarian perceives the patron should have – qualitative judgment • Needs – qualitative judgments better suited to acquisition / preservation • Wants – what the patron wants – quantitative judgment • Wants – quantitative judgments better suited to deselection
The Weeding Algorithm - Parameters • Time period = N = the number of years over which the percent of use will be calculated. • Percent of use = p = the percent use cutoff under which titles are eligible for weeding. • New title age = A = the number of years before a new title is eligible for weeding. • Last use period = L = the number of years from the current year where any use preserves the title
The Weeding Algorithm- Step 1 • Identify the Non-eligible collection (classic texts, local authors, histories, special collections, etc.) - creates the 1stiteration of the core collection. • Only qualitative step • Creates perpetual list to be re-used • Update, if desired, with select new titles
The Weeding Algorithm- Steps 2 - 4 • Apply “New title age” metric, A • Apply “Last use period” metric, L • Apply “Percent of use” metric, p • What remains is weeding list