Narrative Aspects of the Novels by Lucy Maud Montgomery and Eleanor Porter
Bibliographic record
Abstract
The metamodernist mood of the modern era increasingly clearly correlates with the transformation of literary methodologies and methods of reading, analysis, and interpretation of literary texts. The study of the genre specificity of the novel is one of the essential segments of the latest scientific studies, given its flexibility and significant textual representation in various national works of literature. This paper offers a critical review of the key stages in the history of the study of the novel as a literary genre, as well as shows the possibilities of understanding the poetic aspects of the author’s style. The material of the research relies on the novels by the Canadian writer Lucy Maud Montgomery and the US writer Eleanor Porter. Both authors belonged to the same historical and cultural era; their work has many typological parallels due to objective factors. At the same time, the modernist worldview was embodied in each stylistic manner in its own way. Comparison of individual styles makes it possible to carry out a typological analysis within a particular genre with access to the study of common sources of image creation, as well as modeling the interpretive paradigm of metamodernism in the projection on the literature of different historical periods taking into account national characteristics. At the same time, the research opens up the prospect of expanding the methodological horizons of narratology for its progress beyond its “boundaries”: outside of literary studies, in a significant context, as well as in space outside of fiction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".