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Efficient lambda encodings for Mendler-style coinductive types in Cedille

Overview of attention for article published in Electronic Proceedings in Theoretical Computer Science, May 2020
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Article details
Title
Efficient lambda encodings for Mendler-style coinductive types in Cedille
Published in
Electronic Proceedings in Theoretical Computer Science, May 2020
DOI 10.4204/eptcs.317.5
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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 July 2020.
All research outputs
#19,372,859
of 28,100,996 outputs
Outputs from Electronic Proceedings in Theoretical Computer Science
#699
of 2,346 outputs
Outputs of similar age
#270,914
of 415,414 outputs
Outputs of similar age from Electronic Proceedings in Theoretical Computer Science
#16
of 37 outputs
Altmetric has tracked 28,100,996 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,346 research outputs from this source. They receive a mean Attention Score of 1.5. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 415,414 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.