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E-weeding at Academic Libraries: Digital collection management and weeding practices at Swedish university libraries
University of Borås, Faculty of Librarianship, Information, Education and IT.
University of Borås, Faculty of Librarianship, Information, Education and IT.
2022 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Weeding is one of the central tasks of collection management at academic libraries but is often not prioritized. Digital collections are constantly growing and soon there will be a need to manage these resources. This master thesis explores the general state of weeding and e-resource weeding to uncover how these practices are understood and enacted at Swedish academic libraries, while also introducing the term e-weeding.

A mixed method study was conducted and both quantitative and qualitative content analysis were performed on policy documents and interview transcripts respectively. Sentiments, opinions and current practices surrounding weeding and e-weeding at Swedish academic libraries were investigated and discussed.

It was observed that most policy documents did not mention practices that could be understood as e-weeding, but some presented guidelines for print weeding. From the interviews, respondents had no consensus around a definition of e-weeding but practices commonly thought of as digital collection management could be tied to this term. In addition, print weeding was considered by respondents as more acute than e-weeding due to the intangible nature of digital resources. Lastly, it was revealed that criteria used for print weeding could sometimes be used for evaluating digital resources, although unique aspects of e-resources need to be considered. Nevertheless, respondents were open to the idea of e-weeding their digital collections in the future.

Place, publisher, year, edition, pages
2022.
Keywords [en]
weeding, e-weeding, digital collection management, collection management, policy documents, mixed method study, semi-structured interviews, content analysis
National Category
Information Studies
Identifiers
URN: urn:nbn:se:hb:diva-28586OAI: oai:DiVA.org:hb-28586DiVA, id: diva2:1694982
Available from: 2022-09-13 Created: 2022-09-12 Last updated: 2022-09-13Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • harvard-cite-them-right
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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