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Record W2432609838 · doi:10.18438/b8rh0r

Patron Time-Use May Be an Effective Metric for Presenting Library Value to Policy Makers

2016· article· en· W2432609838 on OpenAlexvenueaboutno aff
Ann Glusker

Bibliographic record

VenueEvidence Based Library and Information Practice · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsMetric (unit)LoanComputer scienceStatisticsOperations managementMathematicsBusinessEngineeringFinance

Abstract

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Objective – To test a metric for library use, that could be comparable to metrics used by competing government departments, for ease of understanding by policy makers.
 
 Design – Four types of data were collected and used: Time-diaries, exit surveys, gate counts, and circulation statistics.
 
 Setting – A large public library in British Columbia, Canada.
 
 Subjects – Time-diary subjects were 445 patrons checking out materials; exit survey subjects were 185 patrons leaving the library.
 
 Methods – A paper-based time diary, prototypes of which were tested, was given to patrons who checked out library materials during a one-week period. These patrons were charged with recording the use of the checked-out items during the entire three-week loan period. From this information, the average number of hours spent with various types of loaned material (print and audio/DVD) was calculated. The average number of hours spent per item type was then applied to the circulation statistics for those items, across a month, to get a total of hours spent using all circulated material during that month. During the same one-week period of time-diary distribution, exit surveys were conducted by library staff with patrons leaving the library, asking them how long they had spent in the library during their current visit. The average number of minutes per visit was calculated and then applied to the gate count for the month, to get a total number of minutes/hours spent “resident” in the library that month. Adding the totals, a grand total of patron time-use hours was calculated. A monetary value was applied per hour, using the results of a contingent valuation study from Missoula, Montana (Dalenberg et al., 2004), in order to convert hours of library benefit into a dollar figure.
 
 Main Results – There was a 24% response rate for the time diaries (106/445). The diary entries yielded an average of 3.5 hours of time-use per print item, and 1.9 hours per DVD. The range for audio materials was quite wide, and for all item types, a few heavy users skewed the averages. Hours of secondary use (when people other than the original borrower read, listened to, or watched, the materials) were calculated, and represented 13% of the total hours. The average amount of time spent per visit was 42 minutes. Applying these averages to one month of circulation figures and gate counts, respectively, the result was that patrons spent 182,000 hours using library services in one month. Applying dollar amounts of benefit per hours spent, based on the Missoula study, the result was that patrons had received $842,000 of benefit from their use of the library in that month.
 
 Conclusions – This study confirmed that the prototype performance measure of hours of patron use, and refinements in obtaining it, was a useful tool with which to present the case for the value of libraries to policy makers. The study estimates that 90% of library use occurs off-site, and that a surprising proportion of that use is by secondary users. Future studies could refine the collection methodologies even more by collecting demographic information, by mapping user activities during branch visits, and by obtaining better information about secondary users of materials. Future research should also take into account: seasonal effects on borrowing, reading level of borrowers, and possibilities for collecting information in online formats. With these developments, it might be possible to assign “enjoyment levels” to items in library catalogs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.236
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.041
GPT teacher head0.249
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes2
Has abstractyes

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