If What We Do Matters: Motives of Research in Canadian Literature Scholarship
Bibliographic record
Abstract
If What We Do Matters:Motives of Research in Canadian Literature Scholarship Sarah Banting (bio) My title begins with "if," as if to cast doubt on whether Canadian literature scholarship matters. I write as a sometime insider to the discipline of literary studies and the field of Canadian literature, referring to myself as one of an "us" addressed by articles published in the field, and I have long felt strongly that it does matter. But the study I present here was motivated in part by questions posed from outside the discipline about whether or not and how literature scholarship matters. These questions come from a variety of locations, including from casually curious or skeptical outsiders—I hear them from friends wanting to know what research looks like, in English—but also from students hoping to join the field, as they wonder what they are supposed to do in their essays for their English classes. Behind their uncertainty, I hear the question, why: Why would we bother? Why does this text or that analysis matter?1 Meanwhile, as Daniel Coleman and Smaro Kamboureli, Kit Dobson, and others have pointed [End Page 27] out, similar questions are implied by funding bodies that seek a type of tidily instrumental project literature scholarship does not often seem to provide (Coleman and Kamboureli xv–xvii, xx, Dobson 16). Compelling versions of these questions come, too, from an adjacent scholarly field that I also inhabit: Writing In the Disciplines (wid) research (see, for instance, the work of John Swales, David R. Russell, Ken Hyland, Janet Giltrow, Natasha Artemeva, and Heather Graves or the contributors to Berkenkotter, Bhatia, and Gotti's edited collection). Scholars in this field, who are interested in describing the aims, habits of mind, and rhetorical practices characterizing research in various academic disciplines, have conducted some sustained analyses of literature scholarship (see Charles Bazerman, Jeanne Fahnestock and Marie Secor, Susan Peck MacDonald, Laura Wilder, Katja Thieme, Joanna Wolfe). They have largely pursued this project by analyzing professional texts written by literature scholars, especially the published scholarly articles that embody our research. As Margery Fee observes in a recent editorial, their inquiries into our methods of analysis and argument help make explicit what is otherwise tacit and unexplained disciplinary rhetoric ("Spies in the House" np)—unexplained in our professional texts and, importantly, in our classrooms (see also Wilder Rhetorical, Wolfe, Thieme, Banting "Uncomfortable"). The questions raised by their accounts of our work connect to the questions asked by more general audiences (friends, incoming students, funding bodies), because they too can be conceived as questions about how literature scholarship should be understood and valued: How is it supposed to matter for anyone who is not already a professional in the field?2 I want to pursue two specific versions of that question here, each version prompted by the findings of wid research about our discipline. One is, Can literature scholarship legitimately claim to be doing knowledge-making research—and thus claim to matter in a so-called knowledge economy the same way some other disciplines can? Analysts point out, for example, how our interpretive work remains so fascinated with the [End Page 28] individual particularities and complexities of texts and phenomena that we do not generalize in ways that would enable us to coordinate our findings with others'—hence we do not generate a body of agreed-upon scholarly knowledge (MacDonald). The critical terms that we do generate in our efforts to encapsulate precisely what we see going on in our material are observed to be "upward diverging": as they get more abstract, they diverge from terms other literature scholars might use for similar phenomena rather than converging with them in agreement about what is to be studied and how it is to be defined. This divergence apparently happens because our terms are crafted to be precisely appropriate to the specificities of the material we are reading closely rather than being necessarily generalizable (MacDonald 42). When we do converge on key critical terms, the shared terms tend to be broad theoretical concepts—MacDonald cites, for instance, Foucauldian critiques of power and authority—first generated outside of literature scholarship (116). Accordingly, as Katja Thieme puts it...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".