Quests versus Transgressive Journeys: Contradictions of Female and National Identity in Marian Engel’s <i>Monodromos</i> and <i>The Glassy Sea</i>
Bibliographic record
Abstract
Marian Engel was associated with other writers applauded for their attention to female and national identities in an era conditioned by the Canadian nationalist and second-wave feminist movements, yet she seemed to run against the grain of her times. Critics often expressed frustration that they could not extol her literary talents more supportively. In this essay, the author re-reads the novels Engel wrote before and after her Governor General Award winning novel, Bear, that were not received as positively. Her re-reading locates critical resistance in the misreading of quests. Instead of pursuits of female and national identity, the journeys figured in these novels are themselves transgressions, in that their point is to prove the inability of identity to challenge historical forces that make women uncomfortable in national space.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.051 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".