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Explainable and Interpretable Models in Computer Vision and Machine Learning

Overview of attention for book
Attention for Chapter 9: Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening
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1 X user

Citations

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84 Mendeley
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Chapter title
Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening
Chapter number 9
Book title
Explainable and Interpretable Models in Computer Vision and Machine Learning
Published in
The Springer Series on Challenges in Machine Learning, November 2018
DOI 10.1007/978-3-319-98131-4_9
Book ISBNs
978-3-31-998130-7, 978-3-31-998131-4
Authors

Cynthia C. S. Liem, Markus Langer, Andrew Demetriou, Annemarie M. F. Hiemstra, Achmadnoer Sukma Wicaksana, Marise Ph. Born, Cornelius J. König, Liem, C.C.S., Langer, Markus, Demetriou, A.M., Hiemstra, Annemarie M.F., Achmadnoer Sukma Wicaksana, Sukma, Born, Marise Ph., König, Cornelis J.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 84 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 24%
Student > Master 14 17%
Researcher 6 7%
Student > Doctoral Student 4 5%
Lecturer 4 5%
Other 12 14%
Unknown 24 29%
Readers by discipline Count As %
Psychology 20 24%
Computer Science 15 18%
Social Sciences 6 7%
Business, Management and Accounting 5 6%
Design 2 2%
Other 7 8%
Unknown 29 35%
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 24 March 2021.
All research outputs
#17,732,227
of 25,992,468 outputs
Outputs from The Springer Series on Challenges in Machine Learning
#1
of 1 outputs
Outputs of similar age
#285,477
of 449,509 outputs
Outputs of similar age from The Springer Series on Challenges in Machine Learning
#1
of 1 outputs
Altmetric has tracked 25,992,468 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 1 research outputs from this source. They receive a mean Attention Score of 1.0. This one scored the same or higher as 0 of them.
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 449,509 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them