Pre-Recorded Webinar: Open Data Activism in Search of Algorithmic Transparency: Algorithmic Awareness in Practice
2020
- 42Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
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.
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.
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- Usage42
- Abstract Views42
Other Description
This online learning opportunity will begin with an introduction to algorithms, touching on definitions, implications, and the link to digital transparency initiatives followed by a chance for a simple, hands-on experience with pseudocode (no previous coding knowledge required). Presenters Jason Clark and Julian Kaptanian (Montana State University Libraries) will facilitate a discussion on programmer bias, the complexity behind algorithmic decisions, and integrating the concept of algorithmic awareness into teaching and advocacy.This learning opportunity has grown in part out of the robust research conducted by Clark and Kaptanian as a part of an IMLS grant and the positive reception of a previous collaboration between the ACRL Open Research and Digital Collections Discussion Groups, presented earlier in 2019.Recording available here. We hope to continue the learning here in this hands-on webinar that integrates algorithmic awareness into teaching and advocacy.Source:Verletta KernDigital Scholarship Librarian
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