Gun violence, phenomenal reality and parallel worlds: Christina Kallas’s multi-protagonist narratives in The Rainbow Experiment
Bibliographic record
Abstract
The second film in writer–director Christina Kallas’s New York trilogy, which includes 42 Seconds of Happiness (2016) and Paris is in Harlem (forthcoming), The Rainbow Experiment () portrays the powerful but flawed American experiment. Its script chronicles the desires and tawdry failures of teachers, administrators, parents and students of many hues, ethnicities and gender preferences tearing at each other on one horrific day, beginning with an explosion and ending with a double shooting – or so it seems. The implication is that gun violence has become so familiar that it almost fades into the background of collective despair. However, with a world-view that recalls David Lynch and a narrative that imbeds this school saga within its densely urban setting, Kallas counters the seemingly inevitable choices available in this fractured system. An evolving alternative scenario rests its faith on the arrogant but potentially redemptive young. The comically inflected, tragic linear story is scripted in non-linear flashbacks, crosscuts and elaborate split-screens. The film provides portals to an internal reality that posits that life choices, like a movie plot, can be reversed. Overall, The Rainbow Experiment reinforces Kallas’s emergence as a potent and bold voice, redefining and recontextualizing modern film genres.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".