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Record W1482030113 · doi:10.18438/b8cc9c

Demand-Driven Acquisition E-books Have Equal Cost Per Use as Print, but DDA Has Much More Active Use Overall

2015· article· en· W1482030113 on OpenAlexaffvenue
Laura Newton Miller

Bibliographic record

VenueEvidence Based Library and Information Practice · 2015
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceSubject (documents)Usage dataLibrary scienceWorld Wide Web

Abstract

fetched live from OpenAlex

A Review of: Downey, K., Zhang, Y., Urbano, C., & Klinger, T. (2014). A comparative study of print book and DDA e-book acquisition and use. Technical Services Quarterly, 31 (2), 139-160. http://dx.doi.org/10.1080/07317131.2014.875379 Abstract Objective – To compare usage of demand-driven acquisition (DDA) e-books with print books to help determine if one acquisition model better serves the needs of library users and return on investment. Design – Case study. Setting – Library system of a large American public university. Subjects – 22,018 DDA e-book discovery records, 456 purchased e-book records, and 20,030 print item records were examined. Method – The researchers examined usage statistics, circulation statistics, and cost measures of DDA e-books and print books. E-books were purchased in 2012 and print books were purchased by the start of the DDA project (January 2012). Main Results – All but one of the 456 DDA-triggered e-books had repeated use within the first year, totalling 2,484 user sessions. 90% of the triggered e-books had 2-9 user sessions, and over half had at least 4 user sessions. E-books were most used in classes N (fine arts), P (Language and Literature), and R (Medicine). E-books in T (Technology) had a lower percentage of user sessions compared to other subject areas. 712 (3.2%) of the e-books in the discovery pool were used without triggering a purchase. Usage of e-books in the discovery pool (those used but not triggering a purchase) showed a consistent use of e-books by subject. E-books in Class B (Philosophy, Psychology, Religion) were used more in the discovery pool without actually being purchased, suggesting a light use of a wide range of books in this subject area. In contrast, Class R (Medicine) saw less use in the discovery pool than what was actually purchased, suggesting heavier and more focused use of triggered e-books in this area. Only 62.5% of the 20,030 purchased print books included in the study were used in the first 1 to 2.5 years they were added to the collection (i.e., 37.5% were not used in that time period). Half of the print books were used no more than once (once or no use), and more than 90% were used fewer than 10 times. Print books in Class Q (Science) contributed to only 7.5% of the total circulations, suggesting print books are underused in this subject area. 10.2% of total circulation of print books in Class R (Medicine) suggests print books are better used in this area. Print acquisition and use occur more often in classes N (Fine Arts) and P (Language and Literature). The average cost for DDA e-books was of $98.52 per book. The average price per print book was $59.53. The unit cost per print book was $17.73 per use. Depending on various measures, cost per use for e-books ranged from $17.73 to $29.15 per use. (If the measurement included the free use of non-triggered DDA books, the cost per use was $18.07, essentially the same as the print cost). Conclusion – Both print books and DDA e-books are proportionately distributed across most subject areas. Although DDA and print cost per use are equal, DDA leads to much more active use overall.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0060.006
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0280.010

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 head0.053
GPT teacher head0.265
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2015
Admission routes2
Has abstractyes

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