Bibliographic record
Abstract
Abstract Films incorporating fairy-tale narratives, characters, titles, images, plots, motifs, and themes date from the earliest history of the cinema, beginning with director Georges Méliès’s Le manoir du diable made in 1896, the year after Auguste and Louis Lumière’s first public showing of their “cinematograph” in Paris in 1895. Fairy tales can be oral (told by people in different geographical locations and at various historical times up to the present) and/or literary (created by known authors) in origin, but they manifest in numerous media, including film. While the Disney formula of innocent persecuted heroines, handsome princes, and happy-ever-afters has dominated popular understandings of such narratives (at least in the English-speaking world), fairy tales need not contain these elements. They concern the fantastic, the magical, the dark, the dreamy, the wishful, and the wonderful. Short and feature length, animated and live action, produced in film stock, video, and digital formats, fairy-tale films have appeared in movie theaters and more recently on television and computer screens. Using Kevin Paul Smith’s classification for literary fairy tales, fairy-tale filmic intertexts can include explicit reference in the title—for example, Duane Journey’s Hansel & Gretel Get Baked (2013); implicit reference in the title—for example, Tarsem Singh Dhandwar’s Mirror Mirror (2012); explicit incorporation into the text—as when Micheline Lanctôt’s Le piège d’Issoudun (2003) includes a play of “The Juniper Tree”; implicit incorporation into the text—as when Steven Spielberg’s A.I.: Artificial Intelligence (2001) has the mechanical child David’s human mother abandon him in the woods, as do Hansel and Gretel’s parents; discussing fairy tales, as in the “Once Upon a Crime” episode of the American television show Castle (2009–2016), when the writer and police talk about what fairy tales really mean; and invoking fairy-tale chronotopes (settings and/or environments)—as in the portions of Guillermo del Toro’s Pan’s Labyrinth (2006) set in the heroine Ofelia’s father’s magical kingdom. Alternatively, filmmakers may re-vision a story, sometimes with new spin, as when Matthew Bright’s Freeway 2 (1999) relocates “Hansel and Gretel” to 1990s America, with two delinquent teen girls fleeing to Mexico, or may create an entirely new tale—like Pan’s Labyrinth, not based on any specific previous literary or traditional fairy tale. This article focuses on the cinema—movies made for theatrical and/or video release—but draws on television and Internet films when they offer telling illustrations. Most examples are from English-language media. Although classic works like director Jean Cocteau’s La belle et la bête (1946) have received considerable attention from cinema studies and the fairy-tale structural analysis of Vladimir Propp (1968) has greatly influenced film analysis, only since the beginning of the 21st century has fairy-tale scholarship merged with film scholarship. Scholars of fairy-tale film often consider adaptation and intermediality in cinematic versions of tales. This article uses the example of director Tarsem Singh Dhandwar’s The Fall (2007), which draws on and references fairy-tale magic to collapse, expand, and generally fictionalize time and space to invoke the postmodern and postcolonial as well as the transnational and transcultural.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".