37.678, Calls: International Journal of Learner Corpus Research - "Special Issue: Replication Studies in Learner Corpus Research" (Jrnl)

Wed Feb 18 13:05:02 UTC 2026

LINGUIST List: Vol-37-678. Wed Feb 18 2026. ISSN: 1069 - 4875.

Subject: 37.678, Calls: International Journal of Learner Corpus Research - "Special Issue: Replication Studies in Learner Corpus Research" (Jrnl)

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Date: 17-Feb-2026
From: Kevin McManus [kevin.mcmanus at pitt.edu]
Subject: International Journal of Learner Corpus Research - "Special Issue: Replication Studies in Learner Corpus Research" (Jrnl)

Journal: International Journal of Learner Corpus Research
Issue: Replication Studies in Learner Corpus Research
Call Deadline: 31-May-2026

Replication Studies in Learner Corpus Research
Special Issue of International Journal of Learner Corpus Research,
Issue 2, 2028
Call for Proposals:
Guest Editors:
Kevin McManus, University of Pittsburgh (kevin.mcmanus at pitt.edu)
Yingying Liu, Ocean University of China (lynnliu430 at outlook.com)
Learner corpus research (LCR) continues to generate important insights
about the learning and use of additional languages in several ways,
including the creation and development of rich learner corpora,
fine-grained analyses of linguistic data, and the assessment of key
hypotheses and theories about language learning. Indeed, these efforts
are known to be critical for cumulative knowledge building in the
field (Larsson & Biber, 2025; Paquot & Callies, 2020). Key to this aim
is the conduct of high-quality replication studies. A replication
study contributes to cumulative knowledge by repeating a previous
study’s research design and methods—with or without changes—collecting
new data, and systematically comparing the previous study’s results
with those from the new study (McManus, 2024a, 2024b; Porte & McManus,
2019). Because one aim of carrying out a replication study is to
determine the extent to which repeating a previous study’s design and
methods leads to comparable patterns of results and conclusions,
replication provides a systematic framework for reconsidering,
refining, extending, and sometimes limiting prior research findings.
This is why replication has long been considered an essential part of
the research process that is key to cumulative knowledge building
(Mackey, 2012; Marsden et al., 2018; Porte, 2012).
In LCR, however, replication studies remain rare, possibly due to
challenges related to data availability, methodological transparency,
and analytical complexity. To address this situation, the
International Journal of Learner Corpus Research will publish a
Special Issue dedicated to replication studies in learner corpus
research. We are particularly interested in close or approximate
replications, in which no more than one (close) or two (approximate)
major modifications are intentionally made to the initial study’s
research design and methods (see Porte & McManus, 2019). Relevant
replication studies may involve the use of a different or expanded
version of a (learner) corpus to address important questions in the
field. In addition, contributions that incorporate open science
practices are preferred, for example open data, materials, and/or
analysis code, as well as study preregistration.
Submission:
Potential contributors should submit a replication study proposal by
email to the Guest Editors by May 31, 2026 (kevin.mcmanus at pitt.edu,
lynnliu430 at outlook.com). Please include the name and email address of
the primary author, along with the names of all contributing authors.
Key deadlines for the special issue are as follows:
Timeline:
Proposals due via email to the Guest Editors: May 31, 2026
Decisions and feedback on proposals: June 15, 2026
Submission of first manuscript: April 1, 2027
Final submissions due: April 1, 2028
Proposals must not exceed 500 words and should include the following:
1. Study to be replicated
2. Justification for replication
3. Type of replication (e.g., close, approximate; see Porte & McManus,
2019)
4. What is being modified in the replication and why
5. Design of replication study (including corpus)
6. Analysis of data (including open science practices)
7. Anticipated impact
Questions can be addressed to the Guest Editors
(kevin.mcmanus at pitt.edu and lynnliu430 at outlook.com).

Linguistic Field(s): Applied Linguistics
                     Computational Linguistics
                     Language Acquisition
                     Text/Corpus Linguistics

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