Mirror Mirror on the Wall: The Bible and Ethics in Jordan Peele’s 2019 film Us
Bibliographic record
Abstract
Jordan Peele’s horror movie, Us, takes moral ambiguity to its logical extreme by creating a world, or, rather, two worlds, in which good and evil mirror each other on multiple planes—the narrative, the visual, the spatial, the racial, the ethical—and by its use of a biblical verse—Jer 11:11. The Wilsons, who inhabit “our” world, view the “Tethered”, who inhabit a nether world, as evil insofar as they have emerged from the shadows to terrorize and kill them. The Tethered, however, view the humans as evil, insofar as they take for granted the ability to enjoy and act in the world. Like the prophet Jeremiah, the film draws the audience’s attention to its own iniquity—and the consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".