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Modeling Morphology With Linear Discriminative Learning: Considerations and Design Choices

Overview of attention for article published in Frontiers in Psychology, November 2021
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
8 X users

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
8 Mendeley
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Title
Modeling Morphology With Linear Discriminative Learning: Considerations and Design Choices
Published in
Frontiers in Psychology, November 2021
DOI 10.3389/fpsyg.2021.720713
Pubmed ID
Authors

Maria Heitmeier, Yu-Ying Chuang, R. Harald Baayen

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users 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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Student > Master 2 25%
Researcher 1 13%
Unknown 3 38%
Readers by discipline Count As %
Linguistics 3 38%
Computer Science 2 25%
Unknown 3 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 21 November 2021.
All research outputs
#8,481,307
of 25,392,582 outputs
Outputs from Frontiers in Psychology
#12,161
of 34,424 outputs
Outputs of similar age
#157,675
of 431,069 outputs
Outputs of similar age from Frontiers in Psychology
#376
of 1,545 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 34,424 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.3. 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 431,069 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 1,545 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.