“I love things just as much as people”: Material Culture and L.M. Montgomery’s Emily Trilogy
Bibliographic record
Abstract
The intersection of material culture with children’s fiction is an underexplored area of study. Lucy Maud Montgomery, the Canadian author most famous for Anne of Green Gables, provides an ideal starting point for such an examination. Most of Montgomery’s twenty novels are categorized as children’s fiction and contain references to hundreds of everyday objects with varying degrees of significance: some hugely symbolic or plot-driving, others seemingly incidental but often with unexpected meaning. Montgomery represented her time and place in her realistic fiction and perhaps had no intention of drawing particular attention to material things. However, I argue that these objects are a significant aspect of her fictional worlds, connecting characters, propelling narratives, and enabling protagonists’ growth and development, as well as illuminating aspects of the culture and society of Canada in the early twentieth century. In applying material culture theory to Montgomery’s fiction, I am using various works to provide anthropological context, sources on children’s literature, and others on Montgomery studies to explore the roles of objects in these works with a particular focus on the Emily trilogy. Using close reading and the application of material culture theory as my methodologies, I examine the significance of objects to the protagonist’s growth as a young woman and her career development despite various barriers, as well as exploring connections between characters and even between author and reader.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| 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".