37.1482, Books: Optimizing Decision Trees for the Analysis of World Englishes and Sociolinguistic Data: Buschfeld and Weihs (2026)

[The LINGUIST List]

LINGUIST List: Vol-37-1482. Mon Apr 20 2026. ISSN: 1069 - 4875.

Subject: 37.1482, Books: Optimizing Decision Trees for the Analysis of World Englishes and Sociolinguistic Data: Buschfeld and Weihs (2026)

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Date: 20-Apr-2026
From: Ellena Moriarty [rfsupport at cambridge.org]
Subject: Optimizing Decision Trees for the Analysis of World Englishes and Sociolinguistic Data: Buschfeld and Weihs (2026)


Title: Optimizing Decision Trees for the Analysis of World Englishes
and Sociolinguistic Data
Series Title: Elements in World Englishes
Publication Year: 2026

Publisher: Cambridge University Press
           http://www.cambridge.org/linguistics
Book URL:
https://www.cambridge.org/ch/universitypress/subjects/languages-linguistics/sociolinguistics/optimizing-decision-trees-analysis-world-englishes-and-sociolinguistic-data?format=HB&isbn=9781009470315

Author(s): Sarah Buschfeld; Claus Weihs

Hardback ISBN:  9781009470315 Pages:  108 Price: U.K. £ 55.00
Hardback ISBN:  9781009470315 Pages:  108 Price: Europe EURO 64.19
Hardback ISBN:  9781009470315 Pages:  108 Price: U.S. $ 70.00
Paperback ISBN:  9781009470353 Pages:  108 Price: U.K. £ 18.00
Paperback ISBN:  9781009470353 Pages:  108 Price: Europe EURO 21.01
Paperback ISBN:  9781009470353 Pages:  108 Price: U.S. $ 23.00

Abstract:

This Element introduces PrInDT (Prediction and Interpretation in
Decision Trees), a statistical approach for modeling relationships
between extra- and intralinguistic variables in World Englishes. It is
based on decision trees and controls their size in a way that they are
easy and straightforward to interpret. Furthermore, PrInDT optimizes
their accuracy so that they best fit the data and can be reliably used
for prediction. Moreover, it can handle unbalanced classes that occur,
for example, when comparing non-standard with standard linguistic
realizations. The various PrInDT functions can deal with
classification and regression tasks and can analyze multiple
endogenous variables jointly, even for models combining classification
and regression. The authors introduce these features in some detail
and apply them to World Englishes and sociolinguistic datasets. As
examples, they draw on L1 child data from England and Singapore as
well as linguistic landscapes data from the Eastern Caribbean island
of St. Martin.

Written In: English (eng)



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