How does her garden grow? : the garden topos and trope in Canadian women's writing
Bibliographic record
Abstract
This study offers additional nuance to the garden topos and trope within nineteenth- and twentieth-century Canadian women's writing and extends the critical discussion of landscape and the garden as archetype in Canadian literature. This dissertation cross-fertilizes literary analysis with garden theory, using the work of such garden historians as John Dixon Hunt, Mark Francis, and Randolph Hester. The argument emphasizes that gardens in literature, like their actual counterparts, are an art of milieu, reflective of their socio-physical contexts. Both real and textual gardens are rhetorical: their content and formal features invite interpretation. A textual garden performs similarly to an actual garden by providing a spatial frame; a means of naturalization; a vivid exemplar of growth, fertility and beauty; a mediation of the artificial and the natural; a space of paradox; and a site of social performance. The specific focus of this study is "domestic gardens": gardens that are intimate, immediate to the home, and part of daily life. Chapter one separates the garden from archetypal models by studying the garden as an actual place (specifically, the backwoods kitchen garden) described in the works of Susanna Moodie and Catharine Parr Traill. Chapter two examines how the garden influences Moodie's and Traill's writing of the "transplanted" female emigrant. Chapter three presents the bower as an important precursor to the domestic garden through Gabrielle Roy's Enchantment and Sorrow (1984) and "Garden in the Wind" (1975). Through the bower, Roy mediates the female artist's ambivalence toward home in her pursuit of independence. Chapter four explores Carol Shields' sanctification of the domestic in her fiction through the concept of paradise as both an ideal setting and a mode of being. Chapter five provides a "garden tour" of the poetry of Lorna Crozier, culminating in the garden as a model for the text itself and for the genre of palimpsest. For these writers, literal and figurative gardens are ways of "planting" their characters and personae, "plotting" their narratives, mediating social conventions, and providing an interpretative lens through which readers may perceive the texts as a whole.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.024 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".