“Hansel and Gretel” Films: Crimes, Harms, and Children
Bibliographic record
Abstract
A brutal narrative of child abandonment, murder, and cannibalism may not seem the conventional stuff of fairy tales to those trained for a Disney-eyed view. Yet that is exactly what “Hansel and Gretel” offers. Film versions across genres, including drama, noir, horror, slasher, thriller, comedy, and adventure, deal seriously with crimes against and harms to children. Many practices and behaviours that endanger and damage people of various ages in all kinds of contexts, including environmental degradation, economic exploitation, and many forms of discrimination, are not proscribed in the formal criminal justice system, and/or are beyond the jurisdiction of public institutions. Many actions and inactions that affect and/or pertain to children’s wellbeing are found as recurring themes and ideas in “Hansel and Gretel” films. In this paper, the authors focus on non-supernatural, live-action films available in English for adult viewers that include child main characters, that is, those whose Hansels and Gretels are clearly below the age of puberty. These films, the authors contend, offer distinctive perspectives on harms to children as individuals and as groups, especially with relation to institutions implicating justice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".