Tracking the Functions of AI as Paradata & Pursuing Archival Accountability
Archiving 2022: Expanding Connections Across Digital Cultural Heritage - Final Program and Proceedings, Vol: 19, Issue: 1, Page: 83-88
2022
- 7Citations
- 60Usage
- 19Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations7
- Citation Indexes7
- Usage60
- Downloads41
- Abstract Views19
- Captures19
- Readers19
- 19
Conference Paper Description
While a familiar term in fields like social science research and digital cultural heritage, 'paradata' has not yet been introduced conceptually into the archival realm. In response to an increasing number of experiments with machine learning and artificial intelligence, the InterPARES Trust AI research group proposes the definition of paradata as 'information about the procedure(s) and tools used to create and process information resources, along with information about the persons carrying out those procedures.' The utilization of this concept in archives can help to ensure that AI-driven systems are designed from the outset to honor the archival ethic, and to aid in the evaluation of off-the-shelf automation solutions. An evaluation of current AI experiments in archives highlights opportunities for paradata-conscious practice.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85141782623&origin=inward; http://dx.doi.org/10.2352/issn.2168-3204.2022.19.1.17; https://library.imaging.org/archiving/articles/19/1/17; https://scholarworks.sjsu.edu/faculty_rsca/3401; https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=4400&context=faculty_rsca; https://dx.doi.org/10.2352/issn.2168-3204.2022.19.1.17
Society for Imaging Science & Technology
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