Non-canonical grammar in Best Paper award winners in engineering

English for Specific Purposes 32 (2013) 157–169

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Non-canonical grammar in Best Paper award winners in engineering William Rozycki a,⇑, Neil H. Johnson b a b

Center for Language Research, University of Aizu, Ikki-machi Tsuruga, Aizuwakamatsu 965-8580, Japan English Language Institute, Kanda University of International Studies, Wakaba 1-4-1, Mihama-ku, Chiba 261-0014, Japan

a r t i c l e

i n f o

Article history: Available online 14 May 2013 Keywords: Non-native speakers of English Engineering discourse Gatekeepers Journal editors Writing for research publication English grammar

a b s t r a c t Non-canonical (NC) grammar from a corpus of 14 Best Paper award winners in software and hardware engineering research published since 2006 in IEEE Transactions is presented and analyzed. Two independent raters, using a standard comprehensive grammar of English as a benchmark, identified the NC usage. Most (co)-authors in the corpus report themselves to be non-native speakers of English (NNSEs), but three of the 14 papers have a selfdescribed native speaker of English as a co-author. The majority of the NC usage falls into patterns which match those reported in spoken English communication among NNSEs. The appearance of simplified grammar (e.g. dropping of articles, lack of concord in number marking between subject and predicate) in published research that has attained Best Paper status in engineering’s most prestigious journals may indicate that the gate-keeper role in engineering now reflects the predominance of non-native speakers in the field. Emailed and personal exchanges with editors and reviewers, and data about the international nature of the engineering industry, are presented to throw light on this phenomenon. The paper closes with advice, based on the corpus analysis and findings, for engineering researchers concerning manuscript preparation, as well as advice on pedagogy for teachers of engineering communication. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Over the past thirty or more years, researchers have developed a clear recognition that an academic discipline or professional field represents a discourse community, and that such a community’s writing practices conform to, and innovate within the boundaries of, the field’s discourse (Bhatia, 1993, 1999; Gunnarsson, Linell, & Nordberg, 1997; Hyland, 1998; Myers, 1989; Selinker, 1979; Swales, 1985, 1990). Advances in the areas of corpus linguistics, text analysis, and genre analysis have allowed fine-grained studies of academic and scientific written genres in specific disciplines, of which Malcolm (1987), Swales and Najjar (1987), Myers (1989), Gosden (1993), and Kuo (1999) are a small sample. In parallel to these developments, ethnographic study of discourse has been applied to various professional and academic fields (e.g. Casanave, 1995; Louhiala-Salminen, 2002; Northcott, 2001). Whatever merits such discursive and ethnographic analysis may have for overall human knowledge, the primary motivating factor for such research has been to provide models and guiding principles for students writing and publishing in specific disciplines. The research reported in this paper rose from a motivation to provide students in the field of computer science with such guidance. The focus of the research reported here came about unexpectedly, arising from a metadiscourse analysis by the authors of a corpus of IEEE Transactions Best Paper award winners in the fields of software and hardware ⇑ Corresponding author. E-mail address: [email protected] (W. Rozycki). 0889-4906/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.esp.2013.04.002

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engineering. The original metadiscourse analysis (presented in Johnson and Rozycki (2010)) led incidentally to the identification of considerable non-canonical English grammar use within that corpus. We term non-canonical any language considered unacceptable by the comprehensive grammar of Quirk, Greenbaum, Leech, and Svartvik (1985); detailed discussion can be found in the Methods section of this paper. This non-canonical grammar thus presented a novel and unplanned direction for further research. The decision to pursue this unexpectedly encountered phenomenon arises from the intense interest that scholars in the past few decades have devoted to the general topic of non-native speakers of English (NNSEs) publishing research in English. Awareness has been raised by exploring how gatekeepers influence the content of texts generated within a scientific discourse community (Myers, 1990); the extent to which reviewers and editors may constrain NNSE-generated submissions to journals (Belcher, 2007; Burrough-Boenisch, 2003; Flowerdew, 2000, 2001; Lillis & Curry, 2010; Wood, 2001); the economic and time burdens shouldered by NNSE researchers in scientific and other academic and professional fields where publication in English is the only option for advancement (Ammon, 2001, 2003); how NNSE scholars react to and accommodate with the judgments of journal reviewers and editors (Lillis & Curry, 2010); and whether native-speaker generated English as a strictly applied model is appropriate for international communication (Seidlhofer, 2001). The research reported below has implications that interact with all these topics. In addition, it aligns with heightened interest in NNSE use of English as a lingua franca (Mauranen, 2012) and the implications inherent in the growth of English for international communication. 1.1. The corpus The corpus consists of 14 Best Paper winners appearing in IEEE Transactions. The Transactions are a series of journals devoted to various fields of engineering, and are often the most prestigious venue for research publication in a given field of engineering: seven IEEE journals were the most frequently cited in their fields according to the 2010 Journal Citation Reports, a rating service offered by Thomson Scientific (as cited in IEEE, 2011). Although the Association for Computing Machinery (ACM) also has a series of journals in which computer scientists frequently publish, the IEEE Transactions series offers the broadest disciplinary range of applications of research. For a computer scientist working in either software or hardware engineering, a Best Paper award in IEEE Transactions is a crowning achievement in a research career, and so Best Papers represent a model of success that appeared to be well worth analyzing. The corpus includes both native-English speaking authors and NNSEs. The titles, venue, page numbers, authors’ names, total word count, and other relevant information on the 14 papers of the corpus are presented in Appendix A. 2. Methods As outlined above, our research data began with the identification of non-canonical grammar usage in some papers of an original corpus of six Best Paper winners published between 2006 and 2008 in the fields of software and hardware engineering. Award winners were identified by using a browser search engine and entering the keywords ‘‘IEEE Transactions Best Paper.’’ This led to authors’ Websites, IEEE Transactions editor pages, or research society Websites, in which were identified the author, title, and venue of individual Best Papers. The first seven papers thus identified were then downloaded electronically from the IEEE database through a university library electronic access portal. One paper was eliminated from the corpus due to its brevity (three pages only); the rest fit the profile of full papers published in the field, and so came to comprise the (original) corpus. Details of the metadiscourse analysis of the six papers of the original corpus appear in Johnson and Rozycki (2010), where a section draws attention to grammar in two of the texts and promises future, more extensive research into such language. To explore this research area, we added articles to the original corpus using the same criteria for selection, choosing any IEEE Transactions Best Paper published in 2006 or after. However, for this expansion we deliberately chose Best Papers with an authorship that appeared to be NNSE, following the method outlined in Wood (2001) that involves analysis of lead author name and affiliation, but which we extended to all co-authors as well. We were able to find eight papers that fit our criteria, thus expanding the corpus of Best Papers to 14. We chose the comprehensive grammar by Quirk et al. (1985) as the canon for grammatical usage. The compilers of this comprehensive grammar of English take great pains in the front matter of their grammar (pp. 1–34) to disavow any intention to make prescriptive judgments. Nevertheless, the compilers make clear that they are presenting ‘‘Standard English’’ (p. 18) which they see as comprising educated, national (rather than regionally-based) versions of both British and American English, with a range of usage that can be labeled from ‘‘acceptable’’ to ‘‘unacceptable’’ (p. 33). In addition to acceptability judgments, they report studying three corpora for frequency of use, namely the Survey of English Usage, the Lancaster-Oslo/ Bergen corpus for British English, and the Brown University corpus for American English. We thus label as ‘‘non-canonical’’ grammar usage considered as unacceptable by Quirk et al. Using this criterion, two raters independently identified noncanonical (NC) grammar use in the eight papers that served as additions to the corpus. Raters then met, compared findings, and reached agreement on all cases in dispute. Generally, raters tried to give broad leeway to stylistic differences and only those cases clearly and unequivocally set forth in Quirk et al. as violations of the canon were coded as NC grammar usage. Appendix B, Section 1 lists all examples of NC grammar identified in the corpus. In Section 2 of Appendix B examples of usage

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are listed that were initially flagged as anomalous but were ultimately not rated as NC because Quirk et al. did not specifically judge the usage as unacceptable. After the NC grammar was identified, all examples were analyzed, and patterns emerged that were then identified. At that point, it seemed prudent to compare our NC instances with any possible anomalies in the native-speaker-authored papers in the first corpus. It was in careful re-reading and rating of presumably native-speaker-authored papers of the first corpus that we made a surprising discovery. All but one paper in the first corpus also carried instances of NC grammar use. 2.1. Difficulty in distinguishing NNSE and NS-generated writing We decided to email individual authors to determine whether NNSE co-authors were responsible for the NC grammar in the six papers in the initial corpus, and to email authors of the expanded part (additional eight papers) of the corpus to confirm the NNSE identity of the authors. At the same time these emailed inquiries were made, we also asked about reviewermandated editing or a check by a native speaker. Appendix C, Section 1 shows the wording of our emailed survey of authors. For purposes of our email inquiries, a native speaker was defined as an author who, in answer to our inquiry, considered himself to be a native speaker, or was identified as such by a co-author. The responses to our emailed inquiries were surprising. Despite our earlier presumption, the sole paper that was completely free of any NC grammar in the original corpus (coded BP3, see Appendix A) turned out to be a single-authored paper written by an NNSE. Another paper of the first corpus (BP1) turned out to be authored entirely by NNSEs, counter to our original assumption of native-speaker (NS) authorship. It appears that the method of distinguishing NS and NNSE authors by studying the author’s name and affiliation (as described in Wood, 2001, pp. 77–78) results in only rough estimates, and requires follow-up inquiries to authors to ensure accuracy. Moreover, a paper in the second corpus (BP10) that we had assumed to be authored entirely by NNSEs in fact had a native speaker among the co-authors. Responses to our follow-up emailed inquiries about which author wrote which section in papers with mixed NS and NNSE author teams gave us nothing useful for sorting out authorship of particular sections. In the responses that we received, authors uniformly stated that all co-authors had contributed equally to all sections, and these authors refrained from assigning responsibility to any one co-author for any particular section. In light of these developments, it seems dubious to make distinctions between NS and NNSE-authored papers. Bearing in mind also that the terms NS and NNSE themselves are contested by some researchers (for a critique of the terminology, see Jenkins, 2000), we treat the total of 14 Best Papers thus represented as a single corpus of IEEE Transactions Best Paper award winners. NNSEs make up the overwhelming majority of authors publishing engineering research internationally, so although our corpus is slightly biased in favor of NNSE authorship, we believe the corpus is valid for analysis as a representative sample. The bias toward NNSE authors is slight, but does exist (86% NNSE authorship for our randomly-chosen original corpus, but 94.2% NNSE authorship for our enlarged corpus). This bias seems acceptable for our purpose, which is to find models appropriate for our students (who are all NNSEs) and to analyze the language of these models to aid instruction in research writing by student engineers publishing for international audiences. Table 1 offers data on the corpus itself; NC grammar within the corpus will be analyzed in the following section. 3. Analysis of non-canonical grammar The approach for the analysis that follows differs from the analytical framework known in the literature as Error Analysis (for standard Error Analysis approach cf. Corder, 1981; James, 1998; Richards, 1984). Rather, we begin with the assumption that the examples of usage we list in Appendix B and analyze below are not ‘errors’. Even within the learner pedagogies of English as a Second Language (ESL) and English as a Foreign Language (EFL), there are strong reasons to avoid the labeling of non-canonical usage as ‘error’ or ‘deficiency;’ see Belz (2002). Especially within the context of the Best Papers, in which the English cited above functions as a tool very successfully applied in international communication by specialists at the top of their field, it is more productive to consider the language as different, but not flawed. Our analysis, then, focuses on how the grammatical usage of these accomplished specialists differs from canonical English.

Table 1 Corpus features. Best Paper-awarded research articles Total corpus Longest paper Shortest paper Average per paper Total (co)authors NNSE authors Highest NC ratio Lowest NC ratio

14 118,261 words 13,534 words 4654 words 8447 words 54 51 (94.2%) Once per 336 words [BP7] None (zero) per 9247 words [BP3]

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3.1. Exclusions It may be useful to first discuss our criteria in terms of what was not included. Our exclusions—that is, cases of language that seemed somehow awkward or inauspicious, but that we did not label as NC because the canonical grammar does not specifically exclude it—involved typographic errors, punctuation, or idiosyncratic style in word choice or order. In all cases following, we highlight the usage in focus by adding underlining.

3.1.1. Typographic error There were two cases of obvious typographic errors that are excluded from our list of NC instances. They are: . . .to be constructed in a significanty smaller area. . . and The avoid excessive computational complexity and run-time, a restart strategy. . .

We excluded these from the NC list because typographical errors tell us little about language production, occur in any human input of text, and only reflect the rigor of proofreading devoted to a given text. 3.1.2. Punctuation exclusions Quirk et al. (1985) state ‘‘. . .[T]here is an element of arbitrariness in punctuation. . .’’ (p. 1614). In particular, for canonical grammar there is considerable leeway in regard to placement of commas within English sentences: [T]here is. . . a great deal of flexibility possible in the use of the comma. . . The comma in fact provides considerable opportunity for personal taste. . . (Quirk et al., 1985 p.1161) For this reason unorthodox punctuation is not regarded as NC in the following example. In conclusion, the HDC technique, as well as any other scheme (e.g., [20]) that assumes the nonlinearity to be estimated is well-modeled by a small number of Taylor series terms fails to work well when the nonlinearity to be estimated is very small. Similarly, the presence of commas following ‘‘example’’ and ‘‘architecture’’ in the example below would aid the reader in comprehension, but canonical grammar seems to allow absence of commas: Section II presents an example pipelined ADC architecture and describes the residue amplifier distortion problem. An option was available to describe the above as a case of NC grammar missing the preposition ‘of’, but that would involve second-guessing the authors’ intent. Instead, we view this as a case of missing commas, and therefore not NC. This approach sets a high bar for NC identification in our corpus and therefore, we believe, increases the rigor of our study. Only cases where a comma comes between a bare subject and its predicate is the sentence labeled by us as NC, since Quirk et al. (1985, p. 1619) specify that such a case is ungrammatical.

3.1.3. Style and word choice exclusions We exclude from our NC list many sentences that may strike some readers as awkward or unusual. We exclude them because only usage specifically denoted as NC by Quirk et al. (1985) is included in the NC list. Examples of word choice excluded from our NC list: . . .while preserving its accuracy. Along the same direction, a Gray-coded bit plane. . . This paper presents a safety driving system that. . .

We also exclude cases where a noun which is usually considered non-count, but which has a countable alternate, occurs. These can reflect specialized usage within a field, and at any rate are not strictly NC. Examples of excluded items: Thus, it is crucial to develop hardware architectures to implement the interpolation procedure. The prior works done at. . .

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3.2. NC usage and patterns After excluding typographic, punctuation, and stylistic anomalies, and applying only the strictest definition of NC grammar, the corpus was found to contain a total of 132 NC tokens. These tokens can be roughly classified as follows. 3.2.1. Article usage This pattern includes all non-canonical article use, that is, article deletion in cases where canonically an article is required, insertion of an article where one is not required, and cases where an indefinite article is used where the definite article is canonical, or vice versa. Some abbreviated examples are below, with underlining of the relevant words where omission occurs: . . .obtained for traditional balanced truncation method. . . . . .is a full-wave Method of Moments in spectral domain [12], assuming. . . . . .in which edge map serves as. . . . . .limited to finite field with elements. . . . . .have shown that soft-decision decoder with a total. . .

Below are examples of unneeded insertion of an article, where the noun is already definite: . . .a modified version of the Safanov’s algorithm. . . . . .of gain in the 99% of the enlarged coverage including satellite pointing errors. . . .as described in the Section II.

There are also cases of insertion which do not match the phonologically-conditioned alternation of indefinite article: . . .and the channel matrix Hi becomes an 1 x N complex vector, denoted as. . . The second term is an constraint between. . .

An example of definite article instead of canonical indefinite article is below; the text had not previously mentioned any button. . . .he/she could stop it by pushing the button, which was set beside the seat.

Tokens in the corpus for the article-related NC pattern total 47. This NC article usage pattern is the most common pattern of NC usage, comprising 35.8% of the total NC tokens in the corpus. 3.2.2. Subject–verb discord Cases of subject–verb discord in number marking comprise this pattern. Some abbreviated examples are: . . .the delays reported by VSBT is more accurate than. . . . . .we assume that the fixed multiplicand satisfy the condition. . . . . .variation of all parameters have been performed. . . Knowing the desired length of each strap and their current values. . . The robot position control procedure that have been previously developed. . . . . .their housing contain conductive material that result in. . .

Total tokens in the corpus for this pattern amount to 25. This figure comprises 18.9% of all NC tokens in the corpus. 3.2.3. Verb usage This is an umbrella term for a variety of NC grammar use regarding verbs, whether as predicates or in dependent clauses, or occurring as gerunds, participles, or infinitives.

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3.2.3.1. Active instead of passive verb or vice versa – five tokens. Examples. . . .denoted by vertical striped point, which locates within a short distance . . . This problem is hard to be solved in conventional inpainting scenarios. . .

3.2.3.2. Transitive verb used without object – three tokens. Example. This could allow, for example, to insert a needle precisely at the center. . .

3.2.3.3. Predicate missing – four tokens. Example. The balanced truncation technique extended to include weighting on the input or output as shown in Fig. 2.

3.2.3.4. Conditional in place of indicative – three tokens. Example. The authors would thank Prof. V. Sreeram. . . for helpful discussions. 3.2.3.5. Participle in place of infinitive – one token. Example. The most effective way of setting the weights is to based the value on the ratio. . .

3.2.3.6. Gerund in place of infinitive – one token. Example. The second step consists in making this piston returning back without any action on the wheel. . .

3.2.3.7. Infinitive in place of participle – one token: Example. . . .situated in a similar relative position to. . . the edge, thus ensure the parallel diffusion. . .

3.2.3.8. Gerund in place of demonstrative – one token. Example. . . .constraints imposed by the feed in the reflectarray, becoming then the phases the only variables to be optimized.

3.2.3.9. Bare infinitive in place of gerund – one token. Example. The undesirable effect of hand shake is even more profound during zooming.

3.2.3.10. Verb in place of noun – one token. . . .method presents an effective converge to a desired solution and is. . .

There are a total of 21 tokens within the broad pattern of NC verb usage. This comprises 15.9% of the total NC in the corpus.

3.2.4. Preposition usage The pattern involves missing, redundant, or non-canonical preposition. Examples:

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This can be accomplished by listening pilot signals. . . . . .is partitioned into to two submatrices. . . . . .it can be regarded content-independent.

NC preposition use comprises 15 tokens in the corpus, or 11.4% of the total. 3.2.5. Determinant–noun number discord This pattern involves modifiers of the determinant class (e.g. some, many, various, each, any, all, a few), that canonically require the referent noun to be marked for number in a specific way. Examples of NC usage are given in abbreviated form: . . .this type of approaches is more economic. . . . . .more than one relays are possible. . . . . .by choosing different period and by independent adjustment of the patch dimensions. . . . . .all possible paths, linked in eight connective manner. . . There are several previous work proposed. . .

There are 13 tokens of this pattern in the corpus. In terms of all tokens, this pattern accounts for 9.8% of the corpus NC grammar tokens. 3.2.6. Adjective–adverb usage This pattern involves use of an adjective in place of a canonically-mandated adverb. Examples: . . .specified by an arbitrary directed acyclic graph. . . .microstep control (proportional pressurizing the diaphragms). . .

There are three tokens of this pattern in the corpus, or 2.2% of the total. 3.2.7. Anomalous occurrences The remaining NC usages involve one-time occurrences. One case involves punctuation, where the placement of a comma between the bare subject and its subsequent predicate violates the canonical grammar: As any stepmotor, when overloaded PneuStep, stalls and skips.

Other cases are difficult to classify, as for example: Differently, since no direct information involved in textural region, only the ordinary SSD. . .

In the case above, it is not clear whether the textual meaning requires a predicate within the subordinate clause or at its end. We therefore leave it unclassified. The six remaining NC cases are similarly difficult to classify. In total, the number of cases that do not fall into a classifiable pattern amount to eight, or only 6% of the total NC count. 4. Discussion 4.1. Comprehension of NC patterns in relation to English as a lingua franca (ELF) In a study of English as a lingua franca, Seidlhofer (2004) has identified NC usage patterns in NNSE-to-NNSE spoken language (where speakers do not share the same first language) that appears to offer no obstacle to comprehension. The three patterns she highlights are: (1) subject–verb discord; (2) article omission; (3) bare infinitive in place of gerund. The first two patterns are also identified in the most recent ELF corpus research (Mauranen, 2012). These patterns reported for NNSE spoken interaction also occur in our written Best Paper corpus. Let us examine some specific NC usage

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to consider why, at the linguistic level, such NC usage seemingly presents no obstacle to the awarding of a Best Paper. From the reference point of reader comprehension, look at the following example of NC subject–verb discord in the corpus: BP12 [307] The robot position control procedure that have been previously developed for CT applications [3], [4] has been adapted for MRI applications and is described in Section IV. The reader first encounters ‘have’ as the verb in reference to procedure, but further discovers the singular-marked verbs ‘has’ and ‘is’ also referring to this procedure later in the same sentence. If the reader examines the cited [3] and [4] references, the reader learns that the authors are referring to their own previous work on the topic. Thus, the reader may understand the procedure to be singular with some small effort. Alternately, the reader can wait until reaching Section IV and there learn that the procedure is described as a single process. In other words, the ambiguity of singularity or plurality of the procedure does not seem to be insurmountable for the reader, and the matter in any case is clarified later in the text. Regarding article use, English saliently marks definiteness where many other languages do not. Such marking is redundant, as context gives the reader clues to definiteness or lack thereof. Consider the following NC article omission example from the corpus, and the context in which it appears. BP9 [1273]. . . Moreover, to fully take advantage of the assistant information, a compression-oriented edge-based inpainting algorithm is proposed for image restoration, integrating pixel-wise structure propagation and patch-wise texture synthesis. We also construct a practical system to verify the effectiveness of the compression approach in which edge map serves as assistant information. . . In the NC instance cited above, the reader has read in the previous sentence about an edge-based inpainting algorithm. Thus, the reader can understand that ‘‘edge map’’ refers to a mapped application of the edge-based algorithm. In the case of an NC infinitive in place of gerund, the reader has ample information to determine the function of the bare infinitive from the context, as in BP6 [801] The undesirable effect of hand shake is even more profound during zooming. As we see, English has built-in redundancy in marking for number and for marking definiteness or indefiniteness. Thus, the meanings carried by these instances of NC are comprehensible to readers and may not even be noticed except by NS readers and a few highly-proficient NNSE readers. More importantly, does the appearance of NC usage represent a change of norms in English for engineering? We next turn to what portent, if any, the patterns of NC use hold for the written discourse of international engineering. 4.2. Variability, norms, and discourse practice The patterns of NC use identified in the corpus emerged due to their predominance, but this is predominance only when averaged among all the papers in the corpus. Care must be taken not to regard the presence of predominant NC patterns as a necessary characteristic of each and every paper, or in other words, as an emerging norm adopted uniformly by NNSE authors. We can see this is not true by the fact that NNSE-authored BP3 is completely free of NC use. And it is probably not a coincidence that BP10, written by authors whose first language is a Romance language, is completely free of NC article usage. This may be due to a carryover effect from the authors’ first language (L1). For perhaps the same reason of L1 interference, authors in the corpus whose native language is Chinese have a high rate of NC article use. But the picture is not always so clear. Paper BP7 (see Appendix A) had the highest NC rate, and includes seven cases of NC article use. Yet the lead author and several co-authors appear to be native speakers of Spanish, a Romance language in which articles are used systematically (the bionotes at the end of the article make clear that in addition to Spaniards, the co-authors include a Georgian speaker, a German speaker, and a French speaker). Thus, the presence or absence of the NC patterns in any given paper clearly depend on multiple factors. What is certain is the variability of language generated by differing authors within the corpus, even as predominant patterns exist overall. And because papers in the corpus include NS–NNSE jointly authored articles, or are sometimes authored by teams with a mix of first languages, we can only rarely untangle the NC occurrences in terms of specific first-language effects. We are left with an analysis that describes the predominant patterns of NC use, such as NC article use or subject– verb discord, as overall characteristics. These are patterns that match with those identified by earlier researchers studying the spoken interactions of ELF speakers. However, we refrain from re-formulating any NC usage norms in the writing of engineering professionals that might parallel efforts by Jenkins (2000) and Seidlhofer (2004) to define a new core of NC use in spoken ELF. That effort, in essence a proposal that ELF patterns embody new linguistic norms for pronunciation and grammar, has been criticized (e.g. Prodromou, 2008; Park & Wee, 2011) both on theoretical and evidentiary grounds. Also, whether ELF as a designation is applicable to the language of our corpus – these articles are in written form and are created for consumption by both readers who do not share a common language and also readers who do share the same first language of the authors – is debatable. Anna Mauranen at University of Helsinki is now extending the definition of ELF to include written academic language (University of Helsinki, 2011), but it may be more useful to view the language of our corpus through a discourse lens, examining the practices of a discourse community (Swales, 1990). Before we look at the specific practices of engineers, let us look at the larger area of scientific discourse. One aspect of that discourse is research

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for publication, and Wood (2001) calls this variety of English writing—produced by international researchers in science and technology—International Scientific English (IST). Of this variety Wood says: If ISE is not the property of the native speaker but of scientists of any language background, it can become the property of student scientists as well, who will integrate the language into their developing practice. . . in a way which would be impossible if English is seen as the property of the English-speaking world. . .(p. 82) In addition to ‘‘student scientists’’ we can extend Wood’s premise to the most advanced scientists in the field. The bionotes at the end of each IEEE Transactions article in our corpus, required from each contributor, often describe veteran, highly-accomplished scientists as well as junior and ‘‘student scientists’’. It is clear these authors are skillfully appropriating English for their own disciplinary success. Thus it may be useful to view the acceptance of non-canonical grammar in our corpus in relation to evolving discourse and concomitant changing norms within the international community of engineers. The culture and context of engineering as a discipline, and the discourse community of hardware and software engineers, may best situate this phenomenon of NC usage in our corpus. 4.2.1. Features of engineering as a discourse community One feature of the field of engineering is the predominance of NNSEs both worldwide and in English speaking countries. Orr (2003) cites evidence (p. 154) that even in the US, foreign born scholars make up 45% of all Ph.D.s working in engineering, and 43% of all Ph.D.s working in computer science. Moreover, in the IEEE, the largest organization of engineers in the world (formerly the Institute of Electrical and Electronic Engineering) the largest membership region is Region 10, which encompasses East Asia and the Pacific and has 97,000 members. That region makes up 23% of all IEEE members. Other regions, such as Europe, Africa and the Middle East, and Latin America, contribute further to the number of NNSEs in the IEEE as a whole. Outside of IEEE, the numbers are equally telling. Considering that China and India combined produce roughly three times the number of engineers—albeit broadly defined— as produced in the US (though see Wadhwa, 2005 for a critical look at the numbers), it is clear that engineering is a field with a predominance of NNSEs. 4.2.2. Best Paper selection process And how are Best Papers selected? A link on the homepage of one IEEE society is typical of the selection criteria. According to the site (IEEE Signal Processing Society, 2012) ‘‘Judging shall be on the bases of general quality, originality, subject matter, and timeliness.’’ We emailed the editors of all the journals with papers represented in our corpus, asking about the selection process for Best Paper Awards (see the wording of the survey in Appendix C, Section 2). Most responded that they no longer served as editors and declined to answer. Only one editor responded to our survey. In answer to our Question 3 about the extent to which language use is taken into account in the criteria, our respondent (K. Chakrabarty, Duke University) wrote, ‘‘The writing was certainly taken into account whenever I have served on the selection committee.’’ 4.2.3. Gatekeeper views on NC usage Given the paucity of response from editors, further inquiry was required, if only to find why gatekeepers in engineering seem to be more accommodating of NNSE English than gatekeepers in fields such as mass communications (Flowerdew, 2000), and physics (Li, 2006). At IEEE conference banquets and receptions, editors and reviewers were asked about the process. One NNSE reviewer of Transactions papers stated, ‘‘I never pay attention to the language. I am not a native speaker, so I cannot judge if the language is good’’ (K.M., personal communication). Following a seminar in March of 2012, the current editor-in-chief of IEEE Transactions on Parallel and Distributed Systems, I. Stojmenovic, stated (personal communication) that though NC English use is an issue that he is aware of, ‘‘IEEE does not have the resources to address the issue.’’ Thus, due to limited resources, in this editor’s words, if the English is ‘‘good enough’’ it can be published. 4.2.4. Editing requests, NC rate, and L1 language influence We emailed the authors of the corpus papers, asking if any editor or reviewer had required that the paper be checked by a native speaker. We received 11 replies (78% response rate), but among the 11, only one (BP9) replied that they were asked by a reviewer or editor to get a native-speaker to revise the language. This paper, authored by a team of China-based NNSEs, had the second-highest NC rate (one per 411 words) even after the ‘‘native speaker’’ editing. The other 10 papers did not receive any request from reviewers or editors for language changes. It appears that IEEE reviewers and editors, most of them NNSEs, are accepting manuscripts ‘as is’ if the language is reasonably comprehensible. 4.2.5. Pragmatic acceptance vs. critical awareness From both emailed inquiries and personal discussion, it appears that IEEE editors and reviewers remain largely unaware that fierce battles are being fought in print over the issues of language imperialism and the privileges of the native speaker of English. This may not be surprising; the role of engineering is to apply knowledge to solve real-world problems. In this regard it is less theoretical than many other disciplines, and this pragmatic mindset may allow the discourse community to

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accept NC usage without any of the heated exchanges about norms or the counter-arguments about language imperialism that arise frequently in the social sciences and humanities. In the words of Widdowson: [Specialized registers of English for professional and academic uses] do indeed, and necessarily, change over time. But the change is naturally and endonormatively controlled from within by the requirements of communication across the international community of its specialist users. So scientific English changes. . .as the communicative needs of the community of scientists changes. [. . .] There is no need of native-speaker custodians. Widdowson (1997, pp. 143–144)

5. Conclusions We have offered evidence that, without any conscious proponents, and without any explicit philosophy opposing language imperialism, NNSEs in the field of engineering have organically grown a language that allows all language speakers to communicate with success. This accords with a prediction made only a decade ago by Ammon (2003, p. 34). He suggested a future in which a variety of (scientific) English would emerge that would not be controlled or owned by any one group, least of all native speakers of English. This language would have input from ‘‘Chinese, Japanese, French, Spanish, German, and other Englishes’’ (Ammon, 2003, p. 34). Engineering English used in international venues, especially at the highest levels of the field such as we see in Best Papers, meets that description perfectly. 5.1. Implications for teaching and for practice 5.1.1. Implications for students For teachers of engineering students in settings outside Anglophone countries, the findings from this analysis of the language of Best Papers suggest the possibility to partially modify research writing instruction. Teachers will do well to focus on the structure and format of the research paper, and spend perhaps less time on text-level grammar. This is because engineering gatekeepers exhibit a willingness to accommodate NC usage, and readers appear willing to negotiate the meaning of texts with NC usage. Thus, getting students to pay attention to larger issues of structure, format, transitioning, and content issues will be more rewarding than attention on such points as the use of articles. But there is a caveat: such modification will not well serve students planning to work or study in Anglophone areas. Learners who wish to pursue advanced degrees in Anglophone countries must be made aware that there are differences between global and local expectations, and that canonical grammar use will be expected in academia, as a mark of in-group identity. 5.1.2. Implications for practitioners For researchers practicing in the field of engineering, and based outside of Anglophone countries, the same lesson may be taken concerning attention to grammar usage. Ammon (2001) estimates the scientific-editing industry catering to the needs of NNSE writers to be a billion-dollar industry annually. Van Paris (2007) calculates the economic benefit to native English speakers from an English-only scientific publication system at the international level, and estimates that an NS scientist has an economic advantage equal to 937 Euros per person. For NNSE writers, the economic burden of publishing in English is real. In view of the findings reported here on NC usage, researchers might choose to refrain from automatically paying high prices for language editing, saving laboratory resources for other uses. Engineers seeking to publish in English in international journals may consider using available resources for a thorough grammar check only after an editor or reviewer requests such a check. But this suggestion is not intended to denigrate the esteem of canonical English use. That is a currency carrying value, most likely for a long time to come, both within the Anglophone countries and in other countries around the world. But armed with the findings of this research, engineering professionals around the world can weigh options and make choices that realistically balance resources and linguistic expectations. Appendix A BP1: Cooperative communications with outage-optimal opportunistic relaying. IEEE Transactions on Wireless Communications 6(9), September 2007, pp. 3450–3460. Authors: Aggelos Blestas, Hyundong Shin, and Moe. Z. Win [NNSE, confirmed by email] (9121 words). BP2: Digital background correction of harmonic distortion in pipelined ADCs. IEEE Transactions on Circuits and Systems—I: Regular Papers 53(9), September 2006, pp. 1885–1895. Authors: Andrea Panigada and Ian Galton [one author NS, confirmed by email] (7506 words). BP3: Mixed-domain systems and signal processing based on input decomposition. IEEE Transactions on Circuits and Systems—I: Regular Papers, 53(10), October 2006, pp. 215–2155. Author: Yannis Tsividis [NNSE, confirmed by email] (9247 words). BP4: Rate-distortion optimized streaming of packetized media. IEEE Transactions on Multimedia 8(2), April 2006, pp. 390– 404. Authors: Philip A. Chou and Zhourong Miao [one author NS, confirmed by email] (13,534 words).

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BP5: Model order reduction using variational balanced truncation with spectral shaping. IEEE Transactions on Circuits and Systems 53(4), 2006, pp. 879–891. Authors: P. Heydari & M. Pedram [NNSE (assumed – no response to email)] (8673 words). BP6: Integration of digital stabilizer with video codec for digital video cameras. IEEE Transactions on Circuits and Systems for Video Technology 17(7), 2007, pp. 801–813. Authors: H.H. Chen, C.K. Liang, Y.C. Peng, & H.A. Chang [NNSE confirmed by email]. BP7: Dual-polarization dual-coverage reflectarray for space applications. IEEE Transactions on Antennas and Propagation 54(10), October 2006, pp. 2827–2837. Authors: J.A. Encinar, L. Sh. Datashvili, J. Agustín Zornoza, M. Arrebola, M. Sierra-Castañer, J. L. Besada-Sanmartín, H. Baier, & H. Legay. [NNSE (assumed – no response to email)] (6051 words). BP8: Seat belt vibration as a stimulating device for awakening drivers. IEEE/ASME Transactions on Mechatronics 12(5), October 2007, pp. 511–518. Authors: S.Arimitsu, K.Sasaki, H. Hosaka, M. Itoh, K. Ishida, & A. Ito [NNSE (assumed – no response to email)] (4654 words). BP9: Image compression with edge-based inpainting. IEEE Transactions on Circuits and Systems for Video Technology 17(10), October 2007, pp. 1273–1287. Authors: D. Liu, X. Sun, F. Wu, Sh. Li, & Y.Q. Zhang [NNSE confirmed by email; they report at reviewer request, paid for editing] (9870 words). BP10: A new type of motor: Pneumatic step motor. IEEE/ASME Transactions on Mechatronics 12(1), February 2007, pp. 98– 106. Authors: D. Stoianovici, A. Patriciu, D. Petrisor, D. Mazilu, & L. Kavoussi [one author is NS, confirmed by email] (6327 words) BP11: Transmitter optimization for the multi-antenna downlink with per-antenna power constraints. IEEE Transactions on Signal Processing 55(6), June 2007, pp. 2646–2660. Authors: W. Yu and T. Lan [NNSE confirmed by email] (10,342 words) BP12: LPR: A CT and MR-compatible puncture robot to enhance accuracy and safety of image-guided interventions. IEEE/ ASME Transactions on Mechatronics, 13(3), June 2008, pp. 306–315. Authors: N. Zemiti, I. Bricault, C. Fouard, B. Sanchez, & P. Cinquin. [NNSE confirmed by email] (7654 words). BP13: High-speed interpolation architecture for soft-decision decoding of Reed–Solomon codes. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 14(9), September, 2006, pp. 937–950. Authors: Z. Wang and J. Ma [NNSE confirmed by email] (10,461 words). BP14: A framework for heuristic scheduling for parallel processing on multicore architecture: A case study with multiview video coding. IEEE Transactions on Circuits and Systems for Video Technology 19(11), November 2009, pp. 1658–1666. Authors: Y. Pang, L. Sun, J. (Gene) Wen, F. Zhang, W. Hu, W. Feng, & S.Yang. [NNSE confirmed by email] (6279 words). Appendix B Appendix B may be accessed at the link below: http://neilhjohnson.net/ (within the site, under Reviewed Journal Articles) Appendix C 1. [Inquiry mailed to all corresponding authors in the corpus. In case of no response, a second mailing was made, copied to all co-authors whose email address was available.] Dear Dr. _____, My name is William Rozycki and I am a linguistic scientist. I research IEEE Best Papers published in various IEEE Society Transactions. You have been chosen because of your Best Paper Award in _____ for your article __________. Congratulations on this high honor! Please help my research by answering the following four questions about your Best Paper-winning research article. The results may help other international engineers to more easily produce research publications in English. Question 1 Is one or more of the authors of this article a native speaker of English? (If your answer is yes, you do not need to answer the questions below. If your answer is no, please continue to answer the three questions below.) Question 2 Did a native speaker of English check the language of your paper? Question 3 Did the Transactions editor and/or the reviewers of your article recommend you to have the article checked by a native speaker?

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Question 4 Did your laboratory (or department or university) pay money to have your article checked by a native speaker? 2. [Mailing to editors of the journals in the corpus.] Dear Dr. ______, My name is William Rozycki, and I am an IEEE member, a researcher at the University of Aizu. I am researching articles published in IEEE Society Transactions, especially those that won Best Paper awards and were written by non-native speakers of English. You are listed as an editor of IEEE Transactions on ______________________. Since I am studying one or more papers from that year, I hope you will be able to answer the following three quick questions. (1) Did you ever request contributors to have their submissions checked by a native English speaker? (2) Were there any cases of Transaction publications where everyone – the contributors, the reviewers, and the editor – were all non-native speakers of English? (3) In your experience, are Best Paper awards chosen primarily on the basis of the research quality? To what extent, if any, is the quality of the writing (organization, style, grammaticality, word choice) taken into account for a Best Paper award? I thank you in advance for your answers and would be grateful if you could add any other comments on the process that brings research articles by non-native speakers to publication in IEEE Transactions.

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Van Paris, P. (2007). Tackling the Anglophone’s free ride: Fair linguistic cooperation with a global lingua franca. In A. Carli, & U. Ammon (Eds.), Linguistic inequality in scientific communication today (pp. 53–86). AILA, Review 20. Wadhwa, V. (2005). About that Engineering Gap. . . Business Week (13 December 2005). . Widdowson, H. G. (1997). EIL, ESL, EFL: Global issues and local interests. World Englishes, 16(1), 135–146. Wood, A. (2001). International scientific English. In J. Flowerdew & M. Peacock (Eds.), Research perspectives on English for academic purposes (pp. 71–83). Cambridge: Cambridge University Press. William Rozycki is professor and director of the Center for Language Research at the University of Aizu, Aizuwakamatsu, Japan. He received his M.A. and Ph.D. from Indiana University. His research areas are Intercultural Rhetoric and also English for medical, engineering, science, and technology (MEST) purposes. Neil H. Johnson is an assistant director of the English Language Institute at Kanda University of International Studies, Chiba, Japan. He received his Ph.D. in Second Language Acquisition and Teaching from the University of Arizona in 2008. His main research interests are in curriculum development, discourse analysis, and sociocultural theory.