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Other literature type . 2023
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Readability Indices Do Not Say It All on a Text Readability

Authors: Emilio Matricciani;

Readability Indices Do Not Say It All on a Text Readability

Abstract

We propose a universal readability index, GU, applicable to any alphabetical language and related to cognitive psychology, the theory of communication, phonics and linguistics. This index also considers readers’ short-term-memory processing capacity, here modeled by the word interval IP, namely, the number of words between two interpunctions. Any current readability formula does not consider Ip, but scatterplots of Ip versus a readability index show that texts with the same readability index can have very different Ip, ranging from 4 to 9, practically Miller’s range, which refers to 95% of readers. It is unlikely that IP has no impact on reading difficulty. The examples shown are taken from Italian and English Literatures, and from the translations of The New Testament in Latin and in contemporary languages. We also propose an extremely compact formula, relating the capacity of human short-term memory to the difficulty of reading a text. It should synthetically model human reading difficulty, a kind of “footprint” of humans. However, further experimental and multidisciplinary work is necessary to confirm our conjecture about the dependence of a readability index on a reader’s short-term-memory capacity.

Keywords

Electronic computers. Computer science, GULPEASE, alphabetical languages; ARI; English literature; Flesch Reading Ease Index; GULPEASE; human footprint; Italian literature; Miller’s Law; short-term capacity; universal readability index; word interval, human footprint, QA75.5-76.95, ARI, English literature, alphabetical languages, Probabilities. Mathematical statistics, QA273-280, Flesch Reading Ease Index

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    14
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Top 10%
Top 10%
gold