Theory in Practice, or, CanLit Is So Paranoid, You Probably Think This Essay Is about You
Bibliographic record
Abstract
Teoria, the PhD candidate-narrator of Dionne Brand’s Theory (2018), is a distinctly paranoid reader. Their interdisciplinary thesis works to expose the false consciousness that mires others in anti-liberatory stasis. Like Teoria, many Canadian literature scholars are skillful practitioners of hermeneutic suspicion, an approach whereby critique provokes meaningful change by revealing subjects’ complicity with the same ideologies that do them harm. Paranoid reading offers the field a reproducible method for uncovering inequitable systems’ contradictions and slippages. But what if paranoid reading reiterates rather than repairs CanLit’s damage? For all their analytical strength, the hermeneutics of suspicion anchor scholarly analysis to disembodied claims of empirical distance, mastery, and individual refinement, each one a vector for settler-colonial (il)logics. This article challenges paranoid reading’s efficacy as a theory of change: in Canadian literary studies, hermeneutic suspicion both buttresses (settler) scholars’ sense of objective, masterful knowledge and demobilizes Black, queer, and feminist ways of knowing.
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 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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.088 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".