From “Flowery Expression” to Floral Motif: Adapting Discordant Narration in Sarah Polley’s Away from Her
Bibliographic record
Abstract
In her film Away from Her, Sarah Polley subtly transforms the discordant (ideologicallyunreliable) narration of its source text, Alice Munro's "The Bear Came over the Mountain." Given the challenges discordant narration poses for film adaptation, this essay examines how Polley repurposes several elements present in Munro's story, few of which participate in its narration, and consolidates them into a system of cues that undermine the audience's sympathy with the protagonist, thus performing a role equivalent to though technically and rhetorically different from Munro's use of discordance; the most important of these cues constitute the film's floral motif. Overall, Polley's adaptation of discordant narration translates aspects of fictive discourse (sujet) into cinematic content (fabula), a shift that is hypothesized to be common in film adaptations of fiction featuring discordant narrators. More generally, Polley's adaptive manoeuvres illustrate how adaptations can foreground narratological problems and focus theoretical attention on the role of specific narrative elements within and across medial environments. How her film adapts the discordance of Munro's story, mobilizing the ethical and political functions of discordance across contexts, media and discursive forms, endorses the continued use of narratology in the study of adaptation and, furthermore, suggest that Adaptation Studies could help clarify and further our understanding of core concepts in narratology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".