A Maud of her own: re-visioning L.M. Montgomery’s “Western Eden” in Melanie Fishbane’s historical fiction
Bibliographic record
Abstract
This major research paper considers the connection between the genre of historical fiction and the complex dynamics of revisionist history in Melanie Fishbane’s young adult novel Maud: A Novel inspired by the Life of L.M. Montgomery (2017). More specifically, this study critically examines how Fishbane appropriates L.M. Montgomery’s Western Canadian writings for her own purposes to update complex social realities and sensibilities in her historical novel. Because Montgomery’s personal and fictional writings reveal a deeply conflicted and contradictory ideological stance on race issues, particularly where Indigenous peoples are concerned, which may frustrate or alienate 21st century mass readership, Fishbane opted to make her character, Maud, more sympathetic towards the plight of the Indigenous peoples in Prince Albert, Saskatchewan; this revisionist approach, I argue, has potential to gloss over the real Montgomery’s more problematic and more heteroglossic representations on race. This study’s findings indicate that the revisionist nature of historical fiction, moulded by the new context in which it is written, influences the way that texts and historical figures, like L.M. Montgomery are re-imagined and re-written.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".