Bibliographic record
Abstract
This chapter analyzes Vietnamese-Canadian Kim Nguyen’s use of magic in his 2012 movie War Witch/Rebelle . Inspired by the lives of twin brothers from Burma/Myanmar, and shot in and around Kinshasa, Democratic Republic of the Congo, Nguyen’s story about child soldier Komona was ten years in the making. He used Rachel Mwanza, an abandoned child on the streets of Kinshasa, as his lead actress, and added magical touches to the story line. Magic is used in three ways in this movie: to further the plot, when Komona’s voice-over foreshadows events to come; to create horror and mystery, when the ghosts of slaughtered villagers appear; and as symbols of guilt, when Komona’s dead parents appear and stare at her. Komona’s voice-overs describe events that have either just happened or are about to happen, such as the deaths of other children and their parents. Mystery is created when, during combat, the ghosts of dead villagers stare silently at Komona. Past traumas are recalled when Komona’s parents repeatedly appear and stand close by her, during moments of high stress. Overall, magic helps underscore both the vulnerability and resilience of children like Komona, who have been displaced and traumatized by combat.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".