Omizo, Ryan, and William Hart-Davidson. “Finding Genre Signals in Academic Writing.” Journal of Writing Research 7.3 (2016): 485-509. Web. 18 May 2016.
Ryan Omizo and William Hart-Davidson, publishing in a special section on digital text analysis in the Journal of Writing Research, report on a process for investigating markers of genres, specifically in academic writing. They hope to develop a tool that will help advisors and advisees in graduate programs recognize differences between the rhetorical moves made by experienced writers in a field and those more likely to appear in the work of less experienced writers.
They draw on “rhetorical genre theory” to state that although particular kinds of text “recur” in the scholarship of a given field, simply learning patterns for these generic texts does not necessarily produce the kind of text that characterizes expert writing within the field (486). Specific instances of a particular genre vary from the “stable textual patterns” that are easy to identify (486).
As a result, the authors contend, understanding that textual patterns actually constitute rhetorical moves is a necessary component of successfully participating in a genre. Omizo and Hart-Davidson characterize the markers of a genre as “signals shared by author and reader about the social activity—the genre—they are co-negotiating” (486). Understanding the rhetorical purposes of genre features allows novice writers to use them effectively.
The authors work with 505 research articles from the SpringerOpen Journal archive. In order to determine how particular genre markers function as social signals, they begin by developing a coding scheme that mimics what human readers might do in finding clusters of words that do social work within a genre. They give the example of identifying a move essential to an article that can be labeled “science”: “propositional hedging,” in which the writer qualifies a claim to reflect stronger or weaker evidence (487). Omizo and Hart-Davidson argue that in searching for such moves, it is possible to identify a “key protein,” or crucial marker, that indicates the presence of the move (487).
After this initial coding, the authors analyze the texts and convert the markers they find to a graph that allows them to calculate “the relationships between words” (487), which then make visible similarities and differences between the uses of markers in expert work and in novice work, with the intention of allowing advisors and advisees to address the reasons for differences (489).
Their study addresses citation styles in chemistry and materials science (502). They argue that citations are among important kinds of “signaling work” that “communicate something about a text’s status as a response to a familiar kind of exigency to a particular audience” (488). They hoped to find “classifiable patterns in citations moves” that varied “consistently” between experienced and novice writers (489).
They review other ways of categorizing in-text citations, some recognizing as many as twelve different uses of citations. For their own purposes, they created four categories of in-text citations that could be recognized from “premarked cue phrases” similar to those used by D. Marcu, who used phrases marked with “although” and “yet” to locate rhetorical moves (491). Omizo and Hart-Davidson’s scheme, they contend, can recognize types of citation moves and assign them rhetorical functions across disciplines, without requiring any specific knowledge of the discipline or field in which the moves occur (490). Moreover, they argue that their system can distinguish between “mentor and mentee texts” (491).
They categorize citations into
- Extractions: This term denotes “an idea paraphrased from source [sic] and attributed via a parenthetical reference” (491). In an extraction, the paraphrase itself does not reference the source. Such a rhetorical choice, they posit, “prioritize[s] the information” rather than the source author[s] as “active agents” (491).
- Groupings: These include “3 or more sources within a parenthesis or brackets” (492). The authors see the social function of groupings as an indication of how the writer or writers locate their work on the topic in question in the larger disciplinary field. As opposed to an extraction, which notes “what particular agents are saying” about a topic, groupings indicate what “a community of scholars is saying” (493). Groupings often facilitate the groundwork laid out in research-article introductions, in particular allowing scholars to establish their ethos as knowledgeable members of the relevant community (493).
- Author(s) as Actant(s): In this category, the author(s) of the source appear in the sentence as subjects or objects. The category also requires a publication date (493). Omizo and Hart-Davidson see this form of citation as “a qualitatively different means to engage with sourced material” (495), specifically allowing the writer of the current paper to interact directly with others in the field, whether to “affirm, extend, complicate, or challenge” (495).
- Non-citations: This category encompasses all other sentences in an article, including references to named authors using pronouns or without specific dates (495). Recognizing that they are leaving out some moves that other coders might classify as citations, the authors argue that the limited “shallow parsing” their program uses allows them to more precisely determine “citational intrusion whereupon authors are making manifest their adherence to research conventions and signaling adjuncts to their arguments” (495). Thus, they exclude such components of a text as an extended discussion that is not marked by citation conventions.
Omizo and Hart-Davidson explain in detail how they convert the citation patterns their program discovers into graphs that allow them to chart the relationships between different citations (496-501). They believe that this process allows them to detect several phenomena that may be useful to advisors aiding students in developing their “scholarly voice” (507). The data suggest that it may be possible to use a coding scheme like the one proposed in this study to amass features that characterize a body of work by experienced writers and compare analogous features of an advisee’s draft in order to detect deviations that signal “that there is something this writer does not know about the ways others in the disciplinary area use” the particular feature, in this case citations (505).
For example, the data indicate that the papers of less experienced writers vary less and adhere to conventions more insistently than do those of more experienced writers, who have been exposed to more genres and whose status allows more deviation (503-04). Advisee papers exhibit more “elaboration” than do those of their mentors; Omizo and Hart-Davidson suggest that the detection of more Author(s)-as-Actant(s) citations signals this feature. Markers at the sentence level such as words like “actually” or “better” can point to the presence of more explicit evaluative stances in the work of the less experienced writers (505).
In sum, the authors propose that digital analysis can detect patterns in the citation practices of novice scholars that point to differences between their work and the work of more established scholars and thus can allow them to focus their revision on the rhetorical moves embodied in these differences.