Bibliographic record
Abstract
It almost sounds like the beginning of a joke, to ask what Stanley Kramer’s 1961 film Judgment at Nuremberg and Quentin Tarantino’s 2009 Inglourious Basterds have in common. One film is a lengthy black and white, Oscar‐winning courtroom drama, the other a recently released, blood‐soaked World War II fantasy. Yet in an essay for a Law and Literature seminar with Dr. Scott last semester, I sought to explore the similarities between these two films in light of their approaches to themes of justice and legality. Despite initial appearances the two films are remarkably alike. Both Kramer and Tarantino use casting in noteworthy ways ‐ choosing their actors not only for their considerable talents but also theircultural cache. Judgment and Basterds also use the framework of Germany and the war to comment not only on the past, but also on the present; Kramer makes an interesting commentary on the dangers of McCarthyism, and Tarantino carefully allies his audience with a character whose actions clearly can be seen as terrorism. Kramer and Tarantino further highlight their views on justice by using language in interesting, and revealing ways. By looking at a film from the past, as well as the present, this essay examines the ways in which filmmakers combine historical events with aspects of the present to raise questions about important current issues; specifically, imposed justice and legality within the context ofboth the Cold War and the War on Terror.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.063 | 0.023 |
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".