Bibliographic record
Abstract
CULTURAL GRAMMARS was, from its very beginnings, conceived at the edge of acceptable grammar.As we struggled for the words that would articulate the present moment in Canadian literary studies, with its conflicted critical conversations about nation, diaspora, and indigeneity, we often found ourselves trailing off into ellipses … or expletives.But the outstanding papers we received in response to our call for papers amply demonstrated to us that we were taking part in a larger conversation, a conversation that was more comprehensive than we first realized, and more comprehensible when shared among many voices.We are convinced that Cultural Grammars, with its inspired and inspiring analyses, will set Canadian cultural criticism on new pathways.We are deeply grateful, first and foremost, to our contributors, who so thoughtfully engaged with the collection's main questions.Through the din of their wise, insurgent, collaborative voices, the contributors demonstrated so powerfully that the constraints of cultural grammars can become possibilities.We are also thankful to Smaro Kamboureli, Canada Research Chair in Critical Studies in Canadian Literature and director of the TransCanada Institute, for encouraging us to pursue this project with rigour and for supporting it as part of her TransCanada series, and to Lisa Quinn, our editor at Wilfrid Laurier University Press, who was in equal parts supportive and exacting in her efforts to help us produce the best possible book.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.430 | 0.287 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".