VRA2014 Collaboration in archives and special collections, Benoit

50 %
50 %
Information about VRA2014 Collaboration in archives and special collections, Benoit
Education

Published on April 23, 2014

Author: VisResAssoc

Source: slideshare.net

Description

Presented by Edward Benoit III at the Annual Conference of the Visual Resources Association, March 12-15, 2014 in Milwaukee, Wisconsin.

Session #10: Case Studies in Collaboration within Archival and Special Collection Environments

MODERATOR: Amanda Grace Sikarskie, Western Michigan University
PRESENTERS:
• Edward Benoit III, University of Wisconsin-Milwaukee
• Jim Cunningham, Illinois State University
• Emily Shaw, University of Iowa
• Amanda Grace Sikarskie, Western Michigan University
Each of the presentations in this session tells a story of collaborations between archivists or special collections librarians and content area scholars. While the content of these speakers’ projects differs greatly—from circus-related images to quilt and embroidery programs on public television to the conceptual art of the Fluxus group—each project benefited from a team approach that made use of various skill sets. Both Jim Cunningham and Amanda Sikarskie worked on digitization projects of collections for which metadata (which was collected in the mid-twentieth century) were initially incomplete, outdated, or just plain inaccurate, prompting partnerships between archivists and content experts at outside institutions. Edward Benoit III’s minimal processing project, on the other hand, dealt with a variety of collections and content areas. It ultimately led to a similar outcome, however, solving the problem of minimal metadata by inviting scholars to participate in social tagging of the collections. Finally, Emily Shaw’s work with the digitization of the Fluxus West collection at the University of Iowa tells the story of forging new relationships through interdepartmental collaboration within a large research university. Please join us for this dynamic session that will be of interest to archivists, librarians, and content experts alike.

Tagging MPLP: A Comparison of Novice & Expert Domain User Generated Tags in a Minimally Processed Digital Photographic Archive Edward Benoit, III School of Information Studies, UW-Milwaukee

Introduction/Background • Howard Zinn • The postmodern archives • Rising backlog problem • Minimal processing/MPLP • Minimally processed digital archives

Study Focus • Supplemental metadata from social tags • User prior domain knowledge as quality control • Research questions: – What are the similarities/differences between tags generated by expert and novices? – In what ways do tags generated by expert/novice users correspond with full metadata? – In what ways do tags generated by expert/novice users correspond with existing users’ query terms?

Methodology • Mixed methods, quasi-experimental two- group design • 60 participants (novice & experts) generate tags for 15 photographs & 15 documents • Pre- and post-questionnaires • Analysis: – Open coding – Descriptive statistics

Sample Collection: Groppi Papers http://collections.lib.uwm.edu/cdm/landingpage/collection/march

Participants • Scoring: – Expert x= 7.57 – Novice x= 2.77 • Ages: 18-63, x= 31.73 • Gender (M/F/O): 23.3%/75%/1.7% Race Frequency % of Participants White 44 73.3% Black 9 15.0% Hispanic/Latino 10 16.7% American Indian 4 6.7% Asian/Indian 2 3.3% Pacific Islander 0 0.0% Other 1 1.7% • 48.3% from WI or IL • 58.3% non-students

Participants’ Prior Use/Knowledge

Coding Scheme • Replication of metadata • Format focused • General identification • Specific identification • Description • Broader context • Emotional Wisconsin Historical Society, WHS-26541 • Image removed for copyright. Accessible at: http://www.wisconsinhi story.org/whi/fullRecor d.asp?id=26541

Results: Number of Tags Total Unique Min Max x Expert 1705 396 15 196 56.83 Novice 2142 291 15 577 71.4 Combined 3847 396 15 577 64.12

Results: Types of Tags Replication Format Gen ID Spec ID Description Broader Emotion Expert 17.54% 0.00% 20.12% 11.79% 31.91% 17.13% 1.52% Novice 14.01% 3.08% 29.43% 12.42% 24.01% 15.74% 1.31%

Results: Matching Metadata % Matching % Non-matching Expert 34.17% 65.83% Novice 25.18% 74.82% Combined 36.69% 63.31%

Results: Matching Queries Match Non- match % Match % Non- match % of Q.T. matching Tags Expert 248 97 71.88% 28.12% 0.58% Novice 184 69 72.73% 27.27% 0.43% Combined 312 147 67.97% 32.03% 0.73% • Query log analysis for one month on existing collection resulted in 42,755 unique query terms

Results: Tagging Motivation How I would find the item How others would find the item The content of the item The item’s format The connection between items The accuracy of the provided information The previous user’s tags My previous tags Expert 4.27 4.10 4.50 3.33 3.43 3.50 3.63 3.87 Novice 4.60 4.60 4.67 3.23 3.63 3.70 3.90 4.10 Combined 4.43 4.35 4.58 3.28 3.53 3.60 3.77 3.98

Results: Motivating Taggers Account & login Newsletter/website recognition Social media recognition Non-monetary rewards Anonymously submission Monetary rewards Expert 3.67 3.30 2.97 3.67 3.87 4.40 Novice 3.07 3.23 2.97 3.83 3.90 4.40 Combined 3.37 3.27 2.97 3.75 3.88 4.40

Conclusion/Future Directions • Replication of presented metadata • Benefits of domain expert tagging • Benefits of including both domain expert and novice tags • Further study needed on: – Alternative factors – How to motive tag generation

Thanks for listening! Please hold your questions for later

Add a comment

Related presentations