Revisiting the ENL-ESL-EFL continuum: A multifactorial approach to grammatical aspect in spoken Englishes

Paula Rautionaho 1 , Sandra C. Deshors 2  and Lea Meriläinen 1
  • 1 University of Eastern Finland,
  • 2 Michigan State University,


This study focuses on the progressive vs. non-progressive alternation to revisit the debate on the ENL-ESL-EFL continuum (i.e. whether native (ENL) and nonnative (ESL/EFL) Englishes are dichotomous types of English or form a gradient continuum). While progressive marking is traditionally studied independently of its unmarked counterpart, we examine (i) how the grammatical contexts of both constructions systematically affect speakers’ constructional choices in ENL (American, British), ESL (Indian, Nigerian and Singaporean) and EFL (Finnish, French and Polish learner Englishes) and (ii) what light speakers’ varying constructional choices bring to the continuum debate. Methodologically, we use a clustering technique to group together individual varieties of English (i.e. to identify similarities and differences between those varieties) based on linguistic contextual features such as AKTIONSART, ANIMACY, SEMANTIC DOMAIN (of aspect-bearing lexical verb), TENSE, MODALITY and VOICE to assess the validity of the ENL-ESL-EFL classification for our data. Then, we conduct a logistic regression analysis (based on lemmas observed in both progressive and non-progressive constructions) to explore how grammatical contexts influence speakers’ constructional choices differently across English types. While, overall, our cluster analysis supports the ENL-ESL-EFL classification as a useful theoretical framework to explore cross-variety variation, the regression shows that, when we start digging into the specific linguistic contexts of (non-)progressive constructions, this classification does not systematically transpire in the data in a uniform manner. Ultimately, by including more than one statistical technique into their exploration of the continuum, scholars could avoid potential methodological biases.

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