Do We Need New Method Names? Descriptions of Method in Scholarship on Canadian Literature
Bibliographic record
Abstract
Literary studies are often seen as a discipline without method. Research articles in literature do not have method sections, nor do they list what type of evidence has been included in a particular project or by what procedures primary material was analyzed. Because of implicitness of questions of method and research design, writing in literary studies is difficult to teach and often relies on students' abilities to infer their own strategies for reading and writing. I analyze a textual corpus of recent research articles from Canadian Literature and Studies in Canadian Literature in order to clarify typical discursive patterns that are used when discussing methods of literary scholarship. On the basis of these findings, we can ask: How can teaching in literary studies be adjusted in order to demystify the methodological practices of the discipline?
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.126 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.029 |
| Science and technology studies | 0.021 | 0.051 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".