Bibliographic record
Abstract
This paper analyzes different forms of intertext (biblical, artistic, and mythological) in L.M. Montgomery’s bestselling novel Anne of Green Gables in order to determine the novel’s intertextual connections with various phenomena of literature and art, and explore how the meanings of these intertextual elements are transformed in Anne of Green Gables as opposed to their original sources. While the plot of Anne Shirley’s growing up unravels locally (in a small Canadian town named Avonlea), it is also part of a broader cultural context, which is represented largely by intertextual means (direct and indirect quotations, allusions to the works by R. Browning, H.C. Andersen, W. Shakespeare, L. Carroll, W. Scott et al.). In this way, the author emphasizes Anne’s romantic worldview, her open-mindedness and vivid interest in literature, art and nature. By referencing the works of W. Shakespeare and S.T. Coleridge, L.M. Montgomery aims to further illustrate the motive of loneliness and abandonment as they are related to her heroine’s story (having lost her parents and spent the majority of her life in an orphan asylum). Biblical intertext also plays an important role when it comes to the relationship between Anne Shirley and Matthew Cuthbert. Different forms of intertext (literary, biblical, mythological) fulfil important functions in the text, especially in terms of creating multi-faceted characters, the social and cultural atmosphere of L.M. Montgomery’s era, and the various problems (social, moral, and artistic) discussed in her works.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".