Casablanca: Judgment and Dynamic Enclaves in Law and Cinema
Bibliographic record
Abstract
By interpreting the narrative and imagery of the film Casablanca, this article seeks to explore the concept of law as enclave. An enclave is a domain-physical, virtual-emotional, conceptual, social or other-defined by certain boundaries and rules of entrance and exit. We argue that Casablanca is about constructing and reconstructing such enclaves. The structure of a pending journey between enclaves organizes the events taking place in Casablanca and constitutes their dynamic nature. Enclaves, we argue, are central to the structure and operation of the law. Recognizing the enclitic nature of law allows us a better grasp of the ethical dimensions of legal practices and reasoning. Further, it makes apparent the oft-overlooked aesthetic dimensions of normative judgments in law (and in film). Our analysis of Casablanca's legal aspects is one example of how law and film may be juxtaposed. Such juxtaposition enriches our understanding of the concepts that structure law and offers a nuanced reading of ethical judgment practiced within the legal and cinematic discourses.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".