Team Snow Queen: feminist cinematic ‘misinterpretations’ of a fairy tale
Bibliographic record
Abstract
Four women directors’ live-action films available in English – Päivi Hartzell’s feature Lumikuningatar (The Snow Queen, 1986), Danishka Esterhazy’s short The Snow Queen (2005), Tamar van den Dop’s feature Blind (2007), and Catherine Breillat’s feature La belle endormie (The Sleeping Beauty, 2010) – use Hans Christian Andersen’s story ‘The Snow Queen’ as hypotext. Explorations of gendered positions in culture and in film, directly linked to a readily identifiable fairy tale, these works offer a compelling example of how geographically and temporally dispersed adaptations can share perspectives beyond their common source material, ones which I argue can be directly linked to what one director felicitously called a (feminist) ‘misinterpretation’ of the original. First, their Snow Queens are lookers in two senses: having a beautiful physical appearance, and actually looking and seeing within the films’ diegeses. Second, they show the Snow Queen’s physical movements and gestures as stylised or unusual. Third, they conflate the characters of Gerda and Kai and/or problematise their gender, making it ambiguous, doubling it, or rendering it in composite. Fourth, unlike Andersen’s story, these films’ climactic scenes focus on interactions/relations between Kai/Gerda and the Snow Queen.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".