Adapting <i>Sunset Song</i>: Authorial, Industrial, and National Discourses in the 2015 Film Adaptation of <i>Sunset Song</i>
Bibliographic record
Abstract
This article traces the discourses shaping the 2015 film adaptation of Sunset Song, directed by Terence Davies. In doing so, it shows how both the film and Lewis Grassic Gibbon's original novel are involved in complex negotiations of ideas about Scottishness. In the case of the film, this is evident in its sophisticated and poetic visual engagement with some aspects of the novel's characterization of Chris Guthrie, its use of language, and its representation of landscapes. The same negotiations are apparent in paratextual materials that demonstrate the route taken by the producers and director when navigating the fraught economic terrain of feature-length filmmaking in Scotland, both in terms of funding applications to national funder Creative Scotland, and the way it mobilized particular discourses of arthouse and auteur cinema in its marketing and production materials. Finally, through a close look at the emphasis on militarism, femininity, and landscape in the film text, the article considers how the film performs a kind of Scotland that is amenable to the tastes of the filmmaker, the desires of the public funder, and the arthouse film festival circuit where it was primarily consumed.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.033 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".