Towards specialized language support: An elaborated framework for Error Analysis

English for Specific Purposes xxx (xxxx) xxx

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English for Specific Purposes journal homepage: http://ees.elsevier.com/esp/default.asp

Towards specialized language support: An elaborated framework for Error Analysis Leigh McDowell a, *, Cassi Liardét b a b

Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia

a r t i c l e i n f o

a b s t r a c t

Article history: Available online xxx

The global rise in academic scholarship and pressure to publish in high-impact Englishmedium journals has led to an increased focus on multilingual scholars and the obstacles they face when communicating across academic and professional domains. Although preparing research for scholarly publication is challenging for most academics, multilingual scholars face the added demands of communicating their work in a foreign language. As one of the largest producers of scientific publications worldwide, Japanese scientists wrestle daily with these challenges and typically employ proofreading as a common coping strategy. Motivated by years of supporting Japanese scientists through the proofreading process, this study employs an Error Analysis (EA) framework, elaborated with the functional descriptions of Systemic Functional Linguistics (SFL), to investigate error patterns in research article manuscripts written by thirteen Japanese materials scientists. Results highlight the difficulties that the nominal group constitutes for participants, with almost half (47.81%) of the identified errors occurring within complex nominal groups. Further, the analysis reveals the most dominant error pattern involves errors with articles and plural -s. Findings from the study inform the design of a pedagogical tool to assist Japanese materials scientists and language specialists alike in identifying and rectifying these errors. Ó 2019 Elsevier Ltd. All rights reserved.

Keywords: English for Research Publication Purposes Copyediting Japanese materials scientists Error Analysis Systemic Functional Linguistics Nominal groups

1. Introduction The number of scholarly outputs in English has risen significantly across recent decades as English continues to grow as the lingua franca of academic scholarship and research (Curry & Lillis, 2017). Although the motivations and pressures to publish in English-medium international journals are prevalent across the globe, the impetus is more pronounced in particular disciplines; for example, Flowerdew and Li (2009) found the trend toward publishing in English more prominent in the physical sciences than in the social sciences and humanities (p. 2; see also Ammon, 2001; Cargill & O’Connor, 2006). Notably, while the majority of English-medium publications within the physical sciences originate in English-speaking countries, such as the United States and the UK, Japan has also been one of the major producers of research articles, ranking as high as second in the world from 1999 to 2009, and remains fifth in the more recent decade from 1996 to 2018 (Scimago, 2019; Thomson-Reuters, 2009). In Japan, English, despite being a foreign language, is the primary professional language for Japanese scientists, and,

* Corresponding author. E-mail address: [email protected] (L. McDowell). https://doi.org/10.1016/j.esp.2019.09.001 0889-4906/Ó 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: McDowell, L., & Liardét, C., Towards specialized language support: An elaborated framework for Error Analysis, English for Specific Purposes, https://doi.org/10.1016/j.esp.2019.09.001

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rather than publishing their work in regional, Japanese-medium publications, Japanese scientists generally favor international English-medium journals with higher impact factors (McDowell & Liardét, 2019; Yamazaki, 1995). For many graduate students and established-career scientists alike, the levels of sophistication required to meet the precision of scientific discourse present significant challenges. For example, in this study, the Japanese participants indicated that they are conscious of producing language that could be perceived by reviewers and readers as errors, detracting from the overall quality of their work (see also McDowell & Liardét, 2019). Research within the emerging field of English for Research Publication Purposes (ERPP) has identified an inherent bias against perceived uses of “non-standard” or “non-native” English (e.g., Lillis & Curry, 2010; Min, 2014). Arguably, linguistic accuracy is not always an impediment for publication acceptance (Belcher, 2007; Rozycki & Johnson, 2013); however, even superficial or minor lexicogrammatical infelicities can accumulate and undermine the authority and intelligibility of a manuscript, possibly even precluding it from further review or consideration (Ehara & Takahashi, 2007). This concern over non-standard language, or linguistic errors, prompts Japanese scientists to seek extensive copyediting of their manuscripts prior to submission for publication (McDowell & Liardét, 2019; Willey & Tanimoto, 2012). Further, among leading English-medium journals, it has become standard advice for non-native Englishspeaking scholars to have their manuscripts reviewed by a “native speaker” prior to submission to ensure clarity and grammatical accuracy (Cargill & O’Connor, 2006; Luo & Hyland, 2016). Although many scholars readily have access to such support, ultimately scientists would benefit from reproduceable resources or tools to allow them to self-edit and refine their own writing. Motivated by the authors’ support for Japanese materials scientists1 in their endeavours to publish their research in English-medium journals, the present study seeks to build on ERPP research and copyediting practices by systematically identifying the most frequent errors in Japanese scientists’ research writing. To achieve this aim, an elaborated Error Analysis framework is devised with the early findings from a pilot study presented to demonstrate how such an approach can inform pedagogical design and multilingual scholar support. This study is part of a larger body of research that ultimately aims to extend knowledge and application of EA in two main ways: (1) by drawing on Systemic Functional Linguistics to update EA’s traditional grammatical descriptions, and (2) by developing the framework of analysis to create a practical yet powerful EA procedure that can be readily applied by ESP practitioners as a form of needs analysis in contexts where grammatical accuracy or precision are important. We begin by reviewing the literature in the emerging field of English for Research Publication Purposes, focusing primarily on the challenges multilingual scholars face when preparing manuscripts for publication. We then outline the elaborated framework of analysis, synthesizing the functional grammatical descriptions of Systemic Functional Linguistics with the methodological procedures of Error Analysis. 1.1. English for Research Publication Purposes (ERPP) The primary language of scientific publication at the beginning of the 20th century was German (Schmidhuber, 2010); however, building on the early successes of British scientists and the rise of scientific scholarship in the United States, English became increasingly prevalent as the century progressed, bolstered by a backlash against the German language following the world wars (Ammon, 2001; Ferguson, 2007). At the turn of the twenty-first century, the use of English in scientific publication only continued to accelerate. For example, in 1995, it was estimated that 87.2% of all journal publications in the natural sciences and 82.5% in the social sciences were written in part or fully in English (Ammon, 2003, p. 244). Less than twenty years later, English accounted for over 95% of journals published in the natural sciences and 90% of those published in the social sciences (Flowerdew, 2013, p. 301; the overall rate of English use is estimated to vary between 75 and 90%, depending on the discipline,; Deng, 2015; van Weijen, 2012). In conjunction with the spread of English as the primary language of scientific research, there has been a rise in the overall number of researchers and scholarly publications. For example, at the beginning of the 20th century, only 293 Americans graduated with a research doctorate, but by the end of the century, the country produced more than 30,000 new science doctorates each year (Thurgood, Golladay, & Hill, 2006). Furthermore, the overall number of researchers grows at about 3% per year, standing at between 7 and 9 million publishing scholars as of 2014 (Ware & Mabe, 2015, p. 6). This explosion of researchers naturally equates to an increase in publications. In 2014, there were about 28,100 active scholarly peer-reviewed English language journals, publishing a collective 2.5 million articles per year (Ware & Mabe, 2015). As of July 2019, Ulrich’s Periodicals Directory indexes 66,636 active peer-reviewed academic journals published in part or fully in English (Serial Solutions, 2019). In this way, as Lillis and Curry (2010) note: “the ever-growing status of the journal and journal articles is paralleled by the ever-growing use of English as the medium of such articles” (p. 9). The prevalence of English as the preferred language of academic research, and in particular, scientific publication, is contrasted with the fact that only 5% of people worldwide are considered native speakers of English (Deng, 2015; Lane, 2018; Lyons, 2017). It follows then that most scholars are publishing their research in English as an additional language (EAL). The difficulties these multilingual scholars face when preparing manuscripts for publication have been widely investigated (e.g., Casanave, 1998; Cho, 2004; Flowerdew, 2000; Li, 2006, 2007). In addition to demonstrating the relevance, competence, and novelty of their research, multilingual scholars “have to meet the often opaque and varying language requirements of these

1

The field of materials science includes research in physics, chemistry, bioscience, device engineering, etc.

Please cite this article as: McDowell, L., & Liardét, C., Towards specialized language support: An elaborated framework for Error Analysis, English for Specific Purposes, https://doi.org/10.1016/j.esp.2019.09.001

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journals, while navigating through the sometimes cryptic, indirect suggestions and comments of reviewers” (Anthony, 2017, p.255; Paltridge, 2015). As the editors of the American Journal Molecular Biology of the Cell point out: This extraordinary imbalance emphasizes the importance of recognizing and alleviating the difficulties faced by nonnative speakers of English if we are to have a truly global community of scientists. For scientists whose first language is not English, writing manuscripts and grants, preparing oral presentations, and communicating directly with other scientists in English is much more challenging than it is for native speakers of English. Communicating subtle nuances, which can be done easily in one’s native tongue, becomes difficult or impossible. (Drubin & Kellogg, 2012, p. 1399, p. 1399) Largely in response to this perceived need to support multilingual scholars’ and the various challenges they face in using English as a professional language, ERPP has emerged as a sub-field of English for Academic Purposes (EAP), addressing “the concerns of professional researchers and post-graduate students who need to publish in peer-reviewed international journals” (Cargill & Burgess, 2008, pp. 75–76). Research in ERPP is typically guided by the theoretical approaches of social constructivism and situated learning, focusing on the writers’ academic literacy practices and experiences (Flowerdew, 2013, pp. 314-314). Investigations often cover the burdens of being a multilingual scholar (e.g., Cho, 2009; Flowerdew, 1999, p. 243; Politzer-Ahles, Holliday, Girolamo, Spychalska, & Berkson, 2016), the benefits of multilingualism (e.g., Belcher, 2007; Kramsch, 1997; Martín, Rey-Rocha, Burgess, & Moreno, 2014; McDowell & Liardét, 2019), the challenges of multilingual collaboration (Bardi, 2015; Curry & Lillis, 2004), the processes of preparing manuscripts (e.g., Bardi, 2015; Hyland, 2012), and the practice of corresponding with journal editors (Cheung, 2010; Sahakyan & Sivasubramaniam, 2008; Sasaki, 2001). Some research in ERPP has highlighted that language is not always a major impediment for publication acceptance, leading practitioners to develop specialized English support beyond the lexico-grammatical and discourse levels, in areas such as study design and acculturation into academic discourse communities (e.g., Belcher, 2007). However, for many multilingual scholars, the linguistic demands of publishing in an additional language remain a source of frustration, leading them to seek specialized language support. This concern over writing problems prompts multilingual scholars, and often their reviewers and editors, to require extensive copyediting of their manuscripts prior to publication (Willey & Tanimoto, 2012). Increasingly, journal websites encourage multilingual scholars to consult with a native English speaker to ensure readability (Cargill & O’Connor, 2006). In examining the different participants in manuscript reviewing and copyediting, Lillis and Curry (2010) coined the phrase “literacy brokers” to describe “all the different kinds of direct intervention by different people, other than named authors, in the production of texts” (p.88). This construct has been further researched, resulting in nuanced descriptions of “author’s editors” (Burrough-Boenisch, 2003), “article shapers” (Burrough-Boenish, 2003; Li & Flowerdew, 2007), “convenience editors” (Willey & Tanimoto, 2012, 2013), “text mediators” (Luo & Hyland, 2016), and “proofreaders” (Harwood, Austin, & Macaulay, 2012). While consulting an outside literacy broker to improve the grammatical accuracy and readability of a manuscript is common,2 it is a time-consuming process that requires significant technical expertise. Further, when reviewing several manuscripts from a specific group of scholars from the same linguistic background, copyeditors will inevitably encounter the same types of errors repeated numerous times. It is this experience and the desire to help Japanese materials scientists become more independent editors of their own writing that motivates the current study. 1.2. Error Analysis and Systemic Functional Linguistics The present study seeks to expand the ERPP knowledge base of the Japanese context by examining the language of Japanese materials scientists who are actively using English for research publication purposes, and in particular, analyzing for patterns of idiosyncratic language by combining the delicate descriptions of Systemic Functional Linguistics with an adapted Error Analysis framework. Error Analysis (EA) is a methodology first introduced in the 1960–70s for the investigation of second language acquisition (Corder, 1967, 1971), and elaborated over subsequent decades (e.g., Abbott, 1980; Dulay, Burt, & Krashen, 1982; James, 1998). With theoretical roots in early Second Language Acquisition (SLA) research, EA was initially applied in the examination of interlanguage (IL). According to the then developing theory, IL (i.e., the learner’s language at a given point in time) was systematic, with rules that may approximate but differ from the target language (Selinker, 1972). Building from this viewpoint, EA was developed and applied to uncover the systems underlying the errors (Corder, 1967, p. 163). Though largely abandoned in SLA research, EA found wider practical applications in subsequent decades within English Language Teaching (ELT), as way to research what James (1998, p. x) highlighted as “a very relevant and everyday professional concern” (i.e., errors). For example, Chen (2006) performed an EA of L1 Taiwanese written essays to measure the pre- and post-treatment effects of a computer-assisted-instruction program on learners’ grammar skills, enabling the researchers to identify the most salient error categories for use in a pedagogical intervention. Similarly, Khodabandeh (2007) used an EA approach to investigate the cross-linguistic influence of L1 Persian on newspaper headline translations into English, examining errors at three linguistic levels (grammatical, lexicosemantic, and discoursal), and adopting an extensive framework of error classification from earlier work (see also Hong, Rahim, Hua, & Salehuddin, 2011).

2

Referred to in this paper as copyediting.

Please cite this article as: McDowell, L., & Liardét, C., Towards specialized language support: An elaborated framework for Error Analysis, English for Specific Purposes, https://doi.org/10.1016/j.esp.2019.09.001

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Although EA remains an effective tool in ELT research, its application and the findings it generates can only be as valid and effective as the analytical frameworks and systems of linguistic descriptions it applies, and one of EA’s most prominent limitations is its reliance on traditional, surface-level grammatical descriptions (Hamilton, 2015; Schleppegrell, 2002). To overcome this, the present study elaborates the traditional EA framework with a Systemic Functional Linguistics approach. Systemic Functional Linguistics (SFL) is a linguistic theory and comprehensive view of language as a socio-semiotic system, which functions in context as a resource for meaning making (Eggins, 2004, p. 3; Matthiessen & Halliday, 2009, pp. 8–16). SFL conceives of language as stratified across three levels of expression: (1) phonology and graphology, (2) lexicogrammar, and (3) discourse semantics. Across this stratified system, SFL identifies three general metafunctions of language, which (1) construe and organize human experience (ideational), (2) enact social relationships (interpersonal) and (3) construct or organize a text (textual; Martin & Rose, 2003). In addition to enriching EA with more delicate grammatical descriptions, SFL enables analyses along functional lines, offering unique insight into the system-structure relationships of grammar (Martin, 2013; Matthiessen & Halliday, 2009). Specifically, SFL linguistic descriptions allow us to understand where the errors occur functionally (e.g., within the nominal group or verbal group), allowing for more insightful analysis of the errors found and how they disrupt the flow of information. Further, as described in Sections 2.3 and 3.1 below, by quantifying the concentration of errors across functional descriptions, we are able to create more meaningful resources that systematically guide authors through the network choices available to achieve accurate expression. Employing an SFL elaborated EA, Schleppegrell (2002) examined the clause-level linguistic resources deployed and errors produced in EAL students’ scientific laboratory reports, finding that errors were a distraction for the instructors evaluating the lab reports, particularly the most frequent errors involving article usage, plural and count/mass noun marking systems, and preposition choice (p. 140). Further, in an SFL-inspired EA of Korean students’ translations from L1 Korean to L2 English, Kim (2010) analyzed clauses according to the three metafunctions, marking and tallying errors to reveal key areas for focused improvement. Kim’s (2010) study demonstrated that using an SFL approach made it possible to classify errors based on meaning and argued for using this knowledge to produce more systematic feedback on errors and competence in translation skills. Similarly, Hamilton (2015) explored the potential contribution of SFL within a traditional EA framework by investigating a corpus of undergraduate French students’ essays, finding that on the morphosyntactic level, most errors occurred within the nominal group, and specifically, manifest as article/determiner errors. Hamilton (2015) concluded that such an SFL-integrated approach to EA yields fresh insight into the understanding of L2 errors. In this way, researchers applying SFL in EA stand to benefit from the decades of theoretical advances made in SFL since EA was devised. 2. The study This study is situated within a graduate school of materials science in a national university in Japan, where one of the authors is involved in English-language education. As a native English speaker with expertise in English teaching, the author is often called on to ‘proofread’3 graduate students’ and faculty members’ research manuscripts before submission to Englishmedium journals. After a number of years observing error patterns consistently occurring across the population, the present authors decided to explore a more efficient way to support these scientists and their need to ensure the accuracydor perhaps more fittingly, the acceptabilitydof their English for research publication. From this motivation, this study aims to characterize the most prevalent error patterns across a sampling of manuscripts, with the greater aim of developing specialized pedagogical resources to inform and apply in ERPP instruction, and potentially as a guide for these scientists to edit their own work. Table 1 Overview of text length. Text

Words per text

Sentences per text

01 02 03 04 05 06 07 08 09 10 11 12 13 Total Average

3,458 4,909 2,881 4,497 4,517 1,851 2,376 5,123 4,137 2,517 1,679 4,144 4,174 46,263 3,559

190 245 154 282 238 96 160 218 315 90 92 218 211 2,509 193

3 ‘Proofreading’ is the term most often used within the Japanese university, however, in this paper the term copyediting is applied to more accurately denote the process.

Please cite this article as: McDowell, L., & Liardét, C., Towards specialized language support: An elaborated framework for Error Analysis, English for Specific Purposes, https://doi.org/10.1016/j.esp.2019.09.001

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The data for this study are research article (RA) manuscripts written by 13 Japanese materials scientists with faculty positions in the graduate school: eight assistant professors, four associate professors, and one full professor. All thirteen participants self-requested English copyediting from the researcher and consented to their manuscripts being analyzed for the purposes of this study. One text per participant was collected for a total of 13 manuscripts. In this study, a manuscript is considered the complete RA just prior to submission for peer-review, including the errors, which have been identified and revised by one of this paper’s authors. As outlined in Table 1, the RA manuscripts vary in length from 1,679 to 5,123 words, averaging 3,559 words4; the total number of sentences per text ranges from 90 to 315, with an average of 193. To account for this range in length, findings throughout this paper are normalized to instances per 1000 words or presented as relative percentages.

2.1. An elaborated framework of analysis The present study analyzes errors in two stages: (1) error recognition and reconstruction, and (2) error classification and quantification. The first stage corresponds with the copyediting process, in which errors are initially identified and revised. Following Corder (1981) and James (1998), errors are recognized as “idiosyncratic” or “unsuccessful language” interrupting the reading flow, and are reconstructed into grammatical forms, based on plausible interpretation elucidated from context. A demonstration of this stage and the annotation used throughout this paper is given in Excerpts 1–2, where Excerpt 1 represents the original text prior to copyediting, and Excerpt 2 illustrates the reconstruction of the errors, with strikethrough and underline identifying deletion and insertion, respectively. The numbers given in square brackets at the end of each excerpt (e.g., [06]) serve as labels identifying the participants’ texts. Before error recognition and reconstruction. 1. Therefore, multiple steps are involved until the acid generation, which also give rise to many decomposed fragments remained in a system for these classes of PAGs. [06] After error recognition and reconstruction. 2. Therefore, multiple steps are involved until the acid generation is achieved, which also giveing rise to many decomposed fragments remaineding in athe system for these classes of PAGs. [06] The second stage of the analysis classifies and quantifies error patterns in the data. Classification uses SFL-based linguistic descriptions; specifically, the rank scale within the lexicogrammatical stratum is applied to identify the five classes of groups/ phrases: (a) nominal group, (b) verbal group, (c) adverbial group, (d) conjugation group, and (e) prepositional phrase (e.g., [nominal group] thin-film transistors [verbal group] are < [adverbial group] often > applied [prepositional phrase] for better performance; for further examples and in-depth explanation of the group/phrase rank, see Halliday & Matthiessen, 2014, pp. 362–363). It also applies the traditional EA four-part classification according to the manner in which the errors occur: (a) omission, (b) addition, (c) selection, and (d) ordering (herein, referred to as error types; e.g., [omission] the sample, [addition] the thermoelectric materials, [selection] limited for to previously-known samples, [ordering] this method can also be also applied; for further examples and in-depth explanation, see Corder, 1981; Dulay et al., 1982). Excerpts 3–6 illustrate the variation with which these different error types occur within the different group/phrase classes: 3. For high-yielding reduction of azides, many conditions have been appeared, e.g. zinc metal/acetic acid, hydrogenolysis, Staudinger reactions, and other reducing agents shown in Scheme 35. [04] 4. Molecular dynamics (MD) simulate simulations suggested three possible binding paths. [08] 5. This means that the most stable conformer in the diastereoselective photoreaction of cyclohexenone can be stabilized efficiently by the clustering effect in the ground state, and may afford highly de values. [03] 6. The weak correlation between structural dissymmetry and the luminescence dissymmetry factor implies a weak contributes contribution of the static-coupling mechanism to the circularly polarized dissymmetry of the lanthanide(III) f-f transition. [10] In Excerpt 3, within the verbal group, there is an addition error of the auxiliary verb be. In Excerpt 4, within the nominal group, there is a selection error, where the verb-form simulate is mis-selected for the noun-form simulation. In Excerpt 5, two errors occur within a single nominal group: the mis-selection of the adverb-form highly for the adjective form high, and the omission of plural -s. Finally, in Excerpt 6 there are four errors: (1) the addition of the definite article the for a non-specific referent in one nominal group (i.e., the luminescence), (2) the omission of both the indefinite article a and (3) the definite article the in another nominal group (i.e., a weak contribution, and the static-coupling mechanism), and (4) the mis-selection

4

Tables, figures and references are not included in these word-counts.

Please cite this article as: McDowell, L., & Liardét, C., Towards specialized language support: An elaborated framework for Error Analysis, English for Specific Purposes, https://doi.org/10.1016/j.esp.2019.09.001

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of the verb form contributes for the noun form contribution. Following classification, errors were quantified across the 13 texts, as presented in Table 2. A total of 1,368 errors were recognized and reconstructed across the thirteen texts, with an average of 31 errors per 1,000 words. The number of errors per text spans from 8 to 59 per 1,000 words, reflecting a wide range in the participants’ written English proficiency. On average, around 32%, or around one in every three sentences contained errors. Although these errors occur across the different clause units (e.g., verbal groups, nominal groups, etc.), they tend to occur more frequently in nominal groups. Importantly, as outlined in Table 2, errors in nominal groups account for almost half of the total errors across the thirteen texts (i.e., 47.81%). In English, and particularly in academic and scientific registers, much of the meaning is packed into nominal groups (Halliday & Martin, 1993; Martin & Veel, 1998). Considering the significance of nominal groups within scientific discourse and the prevalence of errors within this group, the scope of the present examination is restricted to those errors occurring within the nominal group. The analysis accounts for the occurrence of errors by nominal group type (i.e., simple or complex), followed by their location within the nominal group (i.e., Pre-modifier, Head or Post-modifier). Finally, the authors identify and account for the most frequent error pattern across the 13 texts. 2.2. Nominal group type and error location within the nominal group In SFL, two types of nominal groups are distinguished: simple and complex. Both nominal group types comprise a Head.5 In simple nominal groups, the Head stands alone, whereas in complex nominal groups, additional meaning is packed into the Pre- and Post-modifier, as illustrated in Table 3. The simple nominal group in Example 1 (Table 3) simply expresses the name of a chemical compound (Phe7) without modification. In contrast, the complex nominal group in Example 2 comprises a Head (syntheses), which is pre-modified following the pre-determined logical pattern: (a) Deictic, (b) Numerative, (c) Epithet, (d) Classifier, (e) Head (see Halliday & Matthiessen, 2014, p. 379). Further meaning can be packed into the nominal group via the Post-modifier. Post-modification of nominal groups is typically realized by embedded prepositional phrases (e.g., from the viewpoint of acid generation, Example 3) or rankshifted clauses (e.g., stabilizing the carbocation intermediate, Example 4). In addition to contributing to the dense packaging of meaning that is a feature of complex nominal groups in academic and technical discourses, these embedded elements provide further potential for error. Notably, nearly 100% of the nominal group errors occur within complex nominal groups. That is, among the 654 nominal group errors, only one error occurred within a simple nominal group. This finding subsequently prompts the analysis to focus on the forms of modification that make the complex nominal group both a central feature of scientific writing and a prime source of error. An examination into the location of the errors within complex nominal groups revealed that 59% were located in the Pre-modifier, 26% in the Post-modifier, and 15% in the Head. From this analysis, one major error pattern emerged involving all three locations, as detailed in the following section. 2.3. Errors with articles and plural -s The most prevalent and frequent error pattern emerging from the data involves articles (i.e., the, a, and an) and plural -s. Of the 654 errors occurring within nominal groups, 442 were categorized within this pattern. In other words, this error pattern accounts for two thirds of all nominal group errors (i.e., 67.58%), and occurs on average 9.6 times per 1,000 words. In terms of

Table 2 Overview of error frequencies. Text

Total errors

Total errors per 1,000 Sentences with words errors

Percentage sentences with errors (%)

Total number of errors NG errors as percentage of total in NGs errors (%)

01 02 03 04 05 06 07 08 09 10 11 12 13 Total Average

130 179 171 138 39 63 40 109 96 89 80 34 200 1,368 105

38 36 59 31 9 34 17 21 23 35 48 8 48

35.79 32.65 50.00 34.04 14.29 41.67 18.75 34.86 22.22 40.00 55.43 12.39 55.45

72 98 79 83 12 29 22 54 54 40 34 8 69 654 50

5

31

68 80 77 96 34 40 30 76 70 36 51 27 117 802 62

31.96%

55.38 54.75 46.20 60.14 30.77 46.03 55.00 49.54 56.25 44.94 42.50 23.53 34.50 47.81%

Following SFL convention, initial capital letters are used to distinguish functional terms from traditional grammatical classes or non-functional terms.

Please cite this article as: McDowell, L., & Liardét, C., Towards specialized language support: An elaborated framework for Error Analysis, English for Specific Purposes, https://doi.org/10.1016/j.esp.2019.09.001

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Table 3 Nominal group modification. Functional element

Deictic

Numerative

Epithet

Classifier

Head

Post-modification

Typical realisation Location Example 1 [12] Example 2 [02] Example 3 [06] Example 4 [06]

determiner Pre-modifier

numeral

adjective

noun

prepositional phrase/rank-shifted clause Post-modifier

these their the

two

optically-active

substance

noun Head Phe7 syntheses use E1-mechanism

from the viewpoint of acid generation stabilizing the carbocation intermediate

total errors, this one error pattern constitutes around 32% of all errors across the analyzed texts. Notably, the high frequency of this error pattern is comparable to other research; for example, in their analysis of L1 Spanish university-level learners, Dotti and O’Donnell (2014) found that article errors accounted for around 10% of all errors. Similarly, Schleppegrell (2002) reported “count/mass noun and article” errors constituted up to 37.87% of the total errors produced by three ESL students (p. 137; see also Chen, 2006; Hamilton, 2015; Hong et al., 2011). These errors with articles occur within both Pre- and Post-modifiers and includes the Head when articles are mis-selected for plural -s in non-specific plural referents, as demonstrated in Excerpts 7–9. 7 the clusters [03] 8 the previous study ies [13] 9 a high levels of circular polarization [10] While there are straightforward cases of plural -s being omitted (e.g., the polymerization of monomers), the mis-selection of singular for plural forms and vice versa highlights an apparent integrated-ness within the article and plural -s systems, prompting a deeper investigation into the systems underlying the grammar. For this, we look to SFL for the mapping of system-structure relationships, referred to as system networks. System networks provide an efficient way to not only elucidate and visualize grammar but also to analyze the errors within those grammatical systems and identify specific error patterns. Figure 1 presents the system network applied in this study for the analysis of errors with articles and plural -s. Following SFL convention (e.g., Martin, 2013, pp. 13–18; Matthiessen & Halliday, 2009, pp. 98–99), the entry condition for this system network is given on the far left-hand side (i.e., the complex nominal group), and the box on the far right-hand side lists the network’s six structural realizations (e.g., the thing, a thing, etc.). The network comprises three systems: DETERMINATION, COUNTABILITY and NUMBER.6 Importantly, the binary choices within the DETERMINATION system occur simultaneously with choices in the COUNTABILITY system, and subsequently in the NUMBER system, as indicated by the curly bracket. Consequently, errors mis-construing referent specificity (i.e., the choice of definite or indefinite article) are intricately bound with errors mis-construing countability and number (i.e., singular, plural or mass). In this investigation, the 442 errors with articles and plural -s were analyzed according to the five features of the system network (i.e., singular, plural, mass, specific, and non- specific) with relative percentages and examples presented in Figure 2. As illustrated in Figure 2, errors with singular referents are distributed relatively evenly between specific and nonspecific referents (i.e., 27.66% and 30.16%, respectively), with a slight trend towards the non-specific referents. This trend, however, is much more pronounced when considering the naturally occurring frequencies of specific and nonspecific referents in English. In large corpora representing general English, for example the Brown Corpus (Francis & Kucera, 1961) and the British National Corpus (BNC, 2007), the definite article occurs around 2.6 times more frequently than the indefinite article. Therefore, if the error frequencies in this study were simply an artefact of naturally occurring frequencies, we would expect errors with specific singular referents to be around 2.6 times more frequent. Since this is not the case, this result indicates that the indefinite article actually presents greater difficulties for Japanese scientists than the definite article. As previously discussed, this elaborated EA framework examines errors further, according to the four traditional error types: omission, addition, selection and ordering (Excerpts 10–13). Note that the example labelled [i] in Excerpt 13 is an invented example, since no ordering errors were identified in the analyzed texts. This absence indicates that the ordering of articles and plural -s presents no difficulty for these Japanese scientists and subsequently, the analysis aims to address the other three error types (i.e., omission, Excerpt 10; addition, Excerpt 11; and selection, Excerpt 12), as demonstrated below. 10. 11. 12 13

6

highly strained cyclobutene skeletons [03] the directional anisotropy [07] a typical reactions for producing a mature enzyme [08] the very weak the catalytic activity [i]

Systems, in SFL, are denoted in all-caps.

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Figure 1. System network for articles and plural -s.

Figure 2. Relative percentages and examples of errors with articles and plural -s. Note: For purposes of this analysis, this representation privileges the DETERMINATION system over COUNTABILITY. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

The frequency and proportionate distribution of omission, addition, and selection errors with articles and plural -s is outlined in Table 4. Notably, of the three error types, the participants struggled most with omission errors (i.e. 74.2%), and in particular, with singular referents (i.e., 58%). The high occurrence of omission errors with both the article and plural -s correlates with previous studies finding L2 learners with article-less L1s (e.g., Chinese, Taiwanese, Korean) frequently omit articles in their writing (Gressang, 2010; Robertson, 2000). Rozycki and Johnson (2013) similarly found the dropping of articles to be a feature of “non-canonical” grammar in research articles written by non-native engineers, and Master (1997) has shown that even advanced learners have difficulties with article omission. In summary, across the thirteen analyzed texts, the article and plural -s error pattern is the most frequent error pattern, occurring on average 34 times in each text (i.e., 9.6 instances per 1,000 words). This result can be expected given the absence of an article system in the participants’ L1 (Butler, 2002, p. 453) and abstract nature of the DETERMINATION and Table 4 Distribution of error types with article and plural -s errors. Error type

Singular referent

Plural referent

Mass referents

Total

Relative percentage

Per 1,000 words

Omission Selection Addition Total

218 35 3 256 58%

87 3 23 113 26%

23 4 46 73 16%

328 42 72 442

74.2% 9.5% 16.3% 100%

7.1 0.9 1.6 9.6

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COUNTABILITY systems (Barner, Inagaki, & Li, 2009), compounded with the naturally high occurrence of articles in English (Berry, 1991). Nevertheless, pedagogical interventions aimed at refining Japanese scientists’ accuracy in research writing need to focus appropriate attention on conventional use of articles and plural -s, as outlined in the following section. 3. Pedagogical application These findings inform the development of a pedagogical tool that can be applied as an intervention, for example, in the form of a workshop aimed at helping Japanese materials scientists revise their most frequent errors. This tool adopts an English for Specific Purposes (ESP) approach through the precise identification and targeting of a specific need (i.e., errors with articles and plural -s) within a specific population (i.e., Japanese materials scientists), and applies field-specific and datadriven learning, with all examples and data drawn directly from this study. Furthermore, the tool is elaborated through SFL with the application of the rank scale, system networks, and functional terms. Based on results from this study, it is organized into two main parts: consciousness-raising tasks (1) around the nominal group, and (2) around errors with articles and plural -s. 3.1. Consciousness-raising tasks around the nominal group In this first part, participants are guided through the centrality of nominal groups in academic writing. For example, the field-specific sample text shown in Figure 3 demonstrates clearly the relatively large proportion of nominal groups. After gaining an appreciation for the prominence of nominal groups in the academic register and its consequent static naturedin contrast with, for example, the more dynamic and verb-driven nature of everyday spoken genresdparticipants engage in the identification of nominal groups in a sample text and in their own writing. In this stage, the concepts of the Premodifier, Head, and Post-modifier, can be introduced, with participants delineating these elements in their samples (see Table 3).

[Met-heme coordination] contributes to [the stability of the structure] and [the ability of electron transfer in cyt c family proteins]. Although [the optical absorption spectra] and [redox potentials] were similar between [monomeric and dimeric WT PA cyt c551], [heme-ligating His and Met] originated from [different protomers in the dimer], similar to [the case of dimeric HT cyt c55]. In [the case of dimeric horse cyt c], [Metheme coordination] was perturbed and [a hydroxide ion] was coordinated to [the heme iron]. [The difference in the heme coordination structure between dimeric PA cyt c551 and dimeric horse cyt c] may be due to [the differences in the stability of the Met-heme coordination bond] and [the rigidity of the loop containing the hemeligating Met]. According to [DSC measurements], [ΔH for the dissociation of dimeric horse cyt c to monomers] exhibited [a large, negative value], whereas [the ΔH values for the dissociation of dimeric PA cyt c551 and dimeric HT cyt c552] were [~0] and [+14 kcal/mol], respectively. [These results] show that [the coordination of Met to the heme] contributes to [stabilization of the dimer] enthalpically. Figure 3. Nominal groups in academic English. Note: This excerpt is one paragraph taken from the discussion section of [05]. Nominal groups are denoted in square brackets and highlighted in red with Post-modifiers in italics. Total words: 182; words in nominal groups: 148 (81%). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Within this stage, the SFL distinction between simple and complex nominal groups is clarified. Participants are informed that the naming of things in simple nominal groups (e.g., hydrogen peroxidase) typically does not present challenges. Rather, it is the modification that generally presents a problem; in particular, the most frequent error pattern identified in this study (i.e., errors with articles and plural -s) falls within the Pre- and Post-modifier, as well as the Head. A glance through the nominal groups identified earlier would further emphasize the prevalence of complex nominal groups and the importance of understanding their modification. 3.2. Consciousness-raising tasks around errors with articles and plural -s This second stage first identifies the most prevalent error pattern, errors with articles and plural -s, with examples taken from this study showing the various ways the errors unfold and their relative frequencies, as illustrated in Table 5. Additional examples can be given to participants to place within the matrix, helping raise their awareness of the two relevant systems governing this area of the grammar: COUNTABILITY (i.e., singular, plural, mass) and DETERMINATION (i.e., specific and non-specific). Given that participants may not be familiar with these concepts, the system network first presented in Figure 2 can be employed to illustrate to participants the grammatical choices that need to be made. Early testing of this system network with Japanese scientists has indicated that while explanation is necessary, the scientists respond well to this schematic representation. The final step in this pedagogical tool is to guide participants through conventional choices and help them both identify and rectify errors in the systems. To aid in this process, a simplified flow-chart was specially developed (Figure 4). The simple framing of the questions is a defining feature to prompt participants to construe the Head noun as countable or noncountable, then as specific or non-specific. Working with real-life samples from the data (see Appendix) and participants’ own writing, the flexibility, or rather choice, in each system should become apparent. That is to say, many nouns in English have preferred or probable forms, but can be tweaked to convey nuanced meaning in context; for example, coffee and chocolate are textbook non-count mass nouns, but are often used in countable form (e.g., the coffees you ordered, or a box of chocolates). Likewise, whether a noun is construed as specific or non-specific shifts, depending on context and importantly, how the speaker or writer perceives the listener or reader’s prior knowledge of the thing. This approach primes the Japanese scientists to consciously make the choices, rather than relying on a complex system of rules and exceptions. Finally, it is fitting to emphasize to workshop participants that this error pattern accounts for two in every three errors within nominal groups, and one in every three errors in total. As such, there are large gains to be made through focusing in this way on this discrete error pattern. 4. Conclusion The widespread application of English for research publication purposes throughout the physical sciences has given rise to unprecedented levels of communication and collaboration beyond traditional borders; however, this now standard tool for research communication continues to present challenges for multilingual scholars striving to meet the high levels of precision demanded in scholarly writing. In the case of the 13 Japanese materials scientists participating in this study, their concern over the acceptability of the English in their manuscripts can be justified, with results revealing an average of 31 errors per 1,000 words. Given that many scholars would be uncomfortable submitting their work for peer-review with this level of imprecision, it seems natural that these Japanese scientists seek copyediting as a primary means of dealing with the issue. Years of supporting Japanese materials scientists through the copyediting process motivated this investigation to explore a more efficient and self-directed process. We applied an SFL approach to elaborate conventional grammatical descriptions and the traditional framework of EA to analyze the error patterns in 13 research articles written by Japanese materials scientists. The analysis revealed that around half of the total errors (i.e., 654 of 1,368) occur within the nominal group, and all but one of these errors occurred within complex nominal groups, highlighting that the modification of nominal groups poses major challenges to Japanese materials scientists. In particular, errors with articles and plural -s, which occur across the Pre- and Post-modifier and include the Head, emerged as the dominant error pattern, accounting for two in every three nominal group errors and occurring on average ten times per text. Following from this analysis, a pedagogical tool was developed to help Japanese materials scientists, and potentially others, identify and address this major error pattern in their own research writing. In addition to this contribution to ERPP

Table 5 Relative frequencies and examples of errors with articles and plural -s.

Singular Plural Mass

Specific

Non-specific

27.66% the Pauli exclusion principle [02] 9.07% the largest binding energies [08] 7.26% the modulation of protein functions [08]

30.16% an increase in high order oligomers [05] 16.55% higher concentrations [08] 9.30% evidences of the direct observation of the MCh effects in the meta-molecule [07]

Note. Column and row headings refer to the referent after error reconstruction.

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Figure 4. Simplified flow-chart demonstrating the decisions that need to be made in the appropriate choice of articles and plural -s. Note: The flow-chart privileges COUNTABILITY over DETERMINATION. In reality, the systems are simultaneous, as shown in the system network (Figure 1).

pedagogy, it is hoped that the methodology applied here can be expanded to include analyses of multiple error patterns across the whole clause within this and other specific-purpose populations, and that ultimately the framework of analysis can be developed into a practical procedure for EA that can be applied by ESP practitioners as a form of needs analysis in contexts, such as ERPP, where grammatical accuracy and technical precision are critical.

Declaration of competing interest None. Please cite this article as: McDowell, L., & Liardét, C., Towards specialized language support: An elaborated framework for Error Analysis, English for Specific Purposes, https://doi.org/10.1016/j.esp.2019.09.001

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Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.esp.2019.09.001. References Abbott, G. (1980). Towards a more rigorous analysis of foreign language errors. International Review of Applied Linguistics in Language Teaching, 18(1–4), 121134. https://doi.org/10.1515/iral.1980.18.1-4.121. Ammon, U. (2001). The dominance of English as a language of science: Effects on other languages and language communities (vol. 84)Berlin: Mouton de Gruyter. Ammon, U. (2003). The international standing of the German language. In J. Maurais, & M. Morris (Eds.), Languages in a Globalising World (pp. 231-249). Cambridge: Cambridge University Press. Anthony, L. (2017). Reflections and future directions in publishing research in English as an additional language: An afterword. In S. Cargill, & S. Burgess (Eds.), Publishing research in English as an Additional Language: Practices, pathways and potentials (pp. 255-258). University of Adelaide Press. Bardi, M. (2015). Learning the practice of scholarly publication in English-A Romanian perspective. English for Specific Purposes, 37, 98-111. https://doi.org/10. 1016/j.esp.2014.08.002. Barner, D., Inagaki, S., & Li, P. (2009). Language, thought, and real nouns. Cognition, 111(3), 329-344. https://doi.org/10.1016/j.cognition.2009.02.008. Belcher, D. D. (2007). Seeking acceptance in an English-only research world. Journal of Second Language Writing, 16(1), 1-22. https://doi.org/10.1016/j.jslw. 2006.12.001. Berry, R. (1991). Re-articulating the articles. ELT Journal, 45(3), 252-259. https://doi.org/10.1093/elt/45.3.252. BNC. (2007). The British national corpus, version 3 (BNC XML edition). Oxford University computing services on behalf of the BNC consortium. Retrieved from http://www.natcorp.ox.ac.uk/. Burrough-Boenisch, J. (2003). Shapers of published NNS research articles. Journal of Second Language Writing, 12, 223-243. https://doi.org/10.1016/S10603743(03)00037-7. Butler, Y. G. (2002). Second language learners’ theories on the use of English articles. Studies in Second Language Acquisition, 24(03), 451-480. https://doi.org/ 10.1017/S0272263102003042. Cargill, M., & Burgess, S. (2008). Introduction to the special issue: English for research publication purposes. Journal of English for Academic Purposes, 7(2), 75-76. https://doi.org/10.1016/j.jeap.2008.02.006. Cargill, M., & O’Connor, P. (2006). Getting research published in English: Towards a curriculum design model for developing skills and enhancing outcomes. Revista Canaria de Estudios Ingleses, 53, 79-94. Casanave, C. P. (1998). Transitions: The balancing act of bilingual academics. Journal of Second Language Writing, 7(2), 175-203. https://doi.org/10.1016/ S1060-3743(98)90012-1. Chen, L. L. (2006). The effect of the use of L1 in a multimedia tutorial on grammar learning: An error analysis of Taiwanese beginning EFL learners’ English essays. Asian EFL Journal, 8(2), 76-110. Cheung, Y. L. (2010). First publications in refereed English journals: Difficulties, coping strategies, and recommendations for student training. System, 38(1), 134-141. https://doi.org/10.1016/j.system.2009.12.012. Cho, S. (2004). Challenges of entering discourse communities through publishing in English: Perspectives of non-native-speaking doctoral students in the United States of America. Journal of Language, Identity and Education, 3(1), 47-72. https://doi.org/10.1207/s15327701jlie0301_3. Cho, D. W. (2009). Science journal paper writing in an EFL context: The case of Korea. English for Specific Purposes, 28(4), 230-239. https://doi.org/10.1016/j. esp.2009.06.002. Corder, S. P. (1967). The significance of learner’s errors. International Review of Applied Linguistics in Language Teaching, 5(1–4), 161-170. https://doi.org/10. 1515/iral.1967.5.1-4.161. Corder, S. P. (1971). Idiosyncratic dialects and error analysis. International Review of Applied Linguistics in Language Teaching, 9(2), 147-160. https://doi.org/10. 1515/iral.1971.9.2.147. Corder, S. P. (1981). Error analysis and interlanguage. Oxford: Oxford University Press. Curry, M. J., & Lillis, T. (2004). Multilingual scholars and the imperative to publish in English: Negotiating interests, demands, and rewards. TESOL Quarterly, 38(4), 663-688. https://doi.org/10.2307/3588284. Curry, M. J., & Lillis, T. (2017). Problematizing English as the privileged language of global academic publishing. In M. J. Curry, & T. Lillis (Eds.), Global academic publishing: Policies, perspectives, and pedagogies (pp. 1-22). Bristol, UK: Multilingual Matters. Deng, B. (2015). English is the language of science. https://slate.com/technology/2015/01/english-is-the-language-of-science-u-s-dominance-means-otherscientists-must-learn-foreign-language.html. (Accessed 4 January 2019). Dotti, F., & O’Donnell, M. (2014, September). Addressing article errors in Spanish learners of English using a learner corpus. TSLL Conference. Iowa State University. Drubin, D. G., & Kellogg, D. R. (2012). English as the universal language of science: Opportunities and challenges. Molecular Biology of the Cell, 23(8), 1399. https://doi.org/10.1091/mbc.e12-02-0108. Dulay, H., Burt, M., & Krashen, S. (1982). Language two. New York, NY: Oxford University Press. Eggins, S. (2004). Introduction to systemic functional linguistics (2nd ed.). London: Continuum. Ehara, S., & Takahashi, K. (2007). Reason for rejection of manuscripts submitted to AJR by international authors. American Journal of Roentgenology, 188, W113-W116. https://doi.org/10.2214/AJR.06.0448. Ferguson, G. (2007). The global spread of English, scientific communication and ESP: Questions of equity, access and domain loss. Ibérica, 13, 7-38. Flowerdew, J. (1999). Problems in writing for scholarly publication in English: The case of Hong Kong. Journal of Second Language Writing, 8(3), 243-264. https://doi.org/10.1016/S1060-3743(99)80116-7. Flowerdew, J. (2000). Discourse community, legitimate peripheral participation, and the nonnative-English speaking scholar. TESOL Quarterly, 34(1), 127150. https://doi.org/10.2307/3588099. Flowerdew, J. (2013). English for research publication purposes. In B. Paltridge, & S. Starfield (Eds.), The Handbook of English for Specific Purposes (pp. 301321). Chichester: John Wiley & Sons, Ltd. Flowerdew, J., & Li, Y. (2009). English or Chinese? The trade-off between local and international publication among Chinese academics in the humanities and social sciences. Journal of Second Language Writing, 18(1), 1-16. https://doi.org/10.1016/j.jslw.2008.09.005. Francis, W. N., & Kucera, H. (1961). Brown Corpus. Retrieved from https://the.sketchengine.co.uk/open/. Gressang, J. E. (2010). A frequency and error analysis of the use of determiners, the relationships between noun phrases, and the structure of discourse in English essays by native English writers and native Chinese, Taiwanese, and Korean learners of English as a Second language. (Doctor of Philosophy). University of Iowa. http://ir.uiowa.edu/etd/507 Halliday, M. A. K., & Martin, J. R. (1993). Writing science: Literacy and discursive power. London: The Falmer Press. Halliday, M. A. K., & Matthiessen, C. M. I. M. (2014). Halliday’s introduction to functional grammar. Oxford: Routledge. Hamilton, C. E. (2015). The contribution of systemic functional grammar to the error analysis framework. TESOL International Journal, 10(1), 11-28. Harwood, N., Austin, L., & Macaulay, R. (2012). Cleaner, helper, teacher? The role of proofreaders of student writing. Studies in Higher Education, 37, 569-584. https://doi.org/10.1080/03075079.2010.531462. Hong, A. L., Rahim, H. A., Hua, T. K., & Salehuddin, K. (2011). Collocations in Malaysian English learners’ writing: A corpus-based error analysis. 3L: The Southeast Asian Journal of English Language Studies, 17(Special Issue), 31-44.

Please cite this article as: McDowell, L., & Liardét, C., Towards specialized language support: An elaborated framework for Error Analysis, English for Specific Purposes, https://doi.org/10.1016/j.esp.2019.09.001

L. McDowell, C. Liardét / English for Specific Purposes xxx (xxxx) xxx

13

Hyland, K. (2012). Welcome to the machine: Thoughts on writing for scholarly publication. Journal of Second Language Teaching and Research, 1(1), 58-68. James, C. (1998). Errors in language learning and use: Exploring error analysis. Harlow: Longman. Khodabandeh, F. (2007). Analysis of students’ errors: The case of headlines. Asian ESP Journal, 3(1), 6-21. https://doi.org/10.4304/tpls.2.1.126-131. Kim, M. (2010). Translation error analysis: A systemic functional grammar approach. In C. Coffin, T. Lillis, & K. O’Halloran (Eds.), Applied Linguistics Methods: A Reader (pp. 84-94). London: Routledge. Kramsch, C. (1997). The privilege of the nonnative speaker (vol. 112, pp. 359-369)Publications of the Modern language Association of America. Lane, J. (2018, Nov 14). The 10 most spoken languages in the world. https://www.babbel.com/en/magazine/the-10-most-spoken-languages-in-the-world. (Accessed 4 January 2019). Li, Y. (2006). A doctoral student of physics writing for publication: A socio-politically-oriented case study. English for Specific Purposes, 25(4), 456-478. https://doi.org/10.1016/j.esp.2005.12.002. Li, Y. (2007). Apprentice scholarly writing in a community of practice: An intraview of an NNES graduate student writing a research article. TESOL Quarterly, 41(1), 55-79. https://doi.org/10.1002/j.1545-7249.2007.tb00040.x. Li, Y., & Flowerdew, J. (2007). Shaping Chinese novice scientists’ manuscripts for publication. Journal of Second Language Writing, 16(2), 100-117. https://doi. org/10.1016/j.jslw.2007.05.001. Lillis, T. M., & Curry, M. J. (2010). Academic writing in a global context: The politics and practices of publishing in English. NY: Routledge. Luo, N., & Hyland, K. (2016). Chinese academics writing for publication: English teachers as text mediators. Journal of Second Language Writing, 33, 43-55. https://doi.org/10.1016/j.jslw.2016.06.005. Lyons, D. (2017, Jul 26). How many people speak English, and where is it spoken?. https://www.babbel.com/en/magazine/how-many-people-speak-englishand-where-is-it-spoken/. (Accessed 4 January 2019). Martin, J. R. (2013). Systemic functional grammar: A next step into the theory: Axial relations. Beijing: Higher Education Press. Martin, J. R., & Rose, D. (2003). Working with discourse: Meaning beyond the clause. London, UK: Continuum. Martin, J. R., & Veel, R. (Eds.). (1998). Reading science: Critical and functional perspectives on discourses of science. London: Routledge. Martín, P., Rey-Rocha, J., Burgess, S., & Moreno, A. I. (2014). Publishing research in English-language journals: Attitudes, strategies and difficulties of multilingual scholars of medicine. Journal of English for Academic Purposes, 16, 57-67. https://doi.org/10.1016/j.jeap.2014.08.001. Master, P. (1997). The English article system: Acquisition, function, and pedagogy. System, 25(2), 215-232. https://doi.org/10.1016/S0346-251X(97)00010-9. Matthiessen, C. M., & Halliday, M. A. (2009). Systemic functional grammar: A first step into the theory. Beijing: Higher Education Press. McDowell, L., & Liardét, C. L. (2019). Japanese materials scientists’ experiences with English for research publication purposes. Journal of English for Academic Purposes, 37, 141-153. https://doi.org/10.1016/j.jeap.2018.11.011. Min, H.-T. (2014). Participating in international academic publishing: A Taiwan perspective. TESOL Quarterly, 48(1), 188-200. https://doi.org/10.1002/tesq. 154. Paltridge, B. (2015). Referees’ comments on submissions to peer-reviewed journals: When is a suggestion not a suggestion? Studies in Higher Education, 40(1), 106-122. https://doi.org/10.1080/03075079.2013.818641. Politzer-Ahles, S., Holliday, J. J., Girolamo, T., Spychalska, M., & Berkson, K. H. (2016). Is linguistic injustice a myth? A response to Hyland (2016). Journal of Second Language Writing, 34, 3-8. https://doi.org/10.1016/j.jslw.2016.09.003. Robertson, D. (2000). Variability in the use of the English article system by Chinese learners of English. Second Language Research, 16(2), 135-172. https://doi. org/10.1191/026765800672262975. Rozycki, W., & Johnson, N. H. (2013). Non-canonical grammar in best paper award winners in engineering. J. Engl. Spec. Purp., 32, 157-169. https://doi.org/10. 1016/j.esp.2013.04.002. Sahakyan, T., & Sivasubramaniam, S. (2008). The difficulties of Armenian scholars trying to publish in international journals. ABAC J., 28(2), 31-51. Sasaki, M. (2001). An introspective account of L2 writing acquisition. In D. Belcher, & U. Connor (Eds.), Reflections on multiliterate lives (pp. 110-120). Clevedon, UK: Multilingual Matters. Schleppegrell, M. J. (2002). Challenges of the science register for ESL students: Errors and meaning-making. In M. J. Schleppegrell, & M. C. Colombi (Eds.), Developing advanced literacy in first and second languages: Meaning with power (pp. 119-142). Mahwah, N.J.: Lawrence Erlbaum Associates. Schmidhuber, J. (2010). Evolution of National Nobel Prize Shares in the 20th Century. arXiv preprint arXiv:1009.2634. . (Accessed 7 January 2019). Scimago. (2019). SJRdScimago Journal & Country Rank. Retrieved from https://www.scimagojr.com/countryrank.php. Selinker, L. (1972). Interlanguage. International Review of Applied Linguistics in Language Teaching, 10, 209-231. https://doi.org/10.1515/iral.1972.10.1-4.209. Serial Solutions. (2019). Ulrichsweb global serials directory. Retrieved July 2019 from http://ulrichsweb.serialssolutions.com/. Thomson-Reuters. (2009). Science Watch. Retrieved from http://archive.sciencewatch.com/dr/cou/2009/09decALL/. Thurgood, L., Golladay, M. J., & Hill, S. T. (2006). U.S. doctorates in the 20th century. Arlington, VA: National Science Foundation. https://wayback.archive-it. org/5902/20150628154910/http://www.nsf.gov/statistics/nsf06319/pdf/nsf06319.pdf Ware, M., & Mabe, M. (2015). The STM report: An overview of scientific and scholarly journal publishing. . (Accessed 4 January 2019). van Weijen, D. (2012 November). The language of (future) scientific communication. Research Trends, 31. https://www.researchtrends.com/issue-31november-2012/the-language-of-future-scientific-communication/. (Accessed 4 January 2019). Willey, I., & Tanimoto, K. (2012). “Convenience editing” in action: Comparing English teachers’ and medical professionals’ revisions of a medical abstract. English for Specific Purposes, 31, 249-260. https://doi.org/10.1016/j.esp.2012.04.001. Willey, I., & Tanimoto, K. (2013). “Convenience editors” as legitimate participants in the practice of scientific editing: An interview study. Journal of English for Academic Purposes, 12, 23-32. https://doi.org/10.1016/j.jeap.2012.10.007. Yamazaki, S. (1995). Refereeing system of 29 life science journals preferred by Japanese scientists. Scientometrics, 33(1), 123-129. https://doi.org/10.1007/ BF02020778. Leigh McDowell is an Associate Professor at the Nara Institute of Science and Technology (NAIST) in Japan, where he teaches research writing, presentation and discussion skills to graduate students and oversees various international programs. His research interests include English for Specific Purposes, English for Research Publication Purposes, Error Analysis, Systemic Functional Linguistics, and Corpus Linguistics. Cassi Liardét is a Lecturer of Linguistics at Macquarie University, Sydney, where she convenes Academic Communication units and oversees the development of the Macquarie University Longitudinal Learner Corpus (MQLLC), a unique diachronic corpus of learner assignments. Her research interests include grammatical metaphor, academic literacy, genre-based pedagogy, Systemic Functional Linguistics and Corpus Linguistics.

Please cite this article as: McDowell, L., & Liardét, C., Towards specialized language support: An elaborated framework for Error Analysis, English for Specific Purposes, https://doi.org/10.1016/j.esp.2019.09.001