“Don’t Pray for Me, Pray for Them!”: Norman Jewison’s <i>In the Heat of the Night</i> and Hollywood “Redneckification” of Anti-Black Racism
Bibliographic record
Abstract
Since winning the Academy Award for Best Picture in 1968, Norman Jewison’s In the Heat of the Night has been considered a landmark of Hollywood civil rights cinema. In Virgil Tibbs, generally considered the silver screen’s first Black detective, Sidney Poitier captured widespread anger over the glacial pace of social change in the early 1960s. Yet In the Heat of the Night also works to reproduce post-war Hollywood’s narrative regionalization of racism, in which discrimination, racial violence, and forms of institutional and structural racism are construed as distinctly southern phenomena. With emphasis on specific production decisions involving Stirling Silliphant’s screenplay, Jewison’s directorial choices, and the calculations of industry executives, this article considers how Hollywood’s “redneckification” of racism works to efface not only histories of racism in the American North and West but also the Canadian racism which marked Jewison’s Toronto childhood and animated his anti-racist sensibilities as a filmmaker.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".