Bibliographic record
Abstract
Abstract While there are multiple possible definitions of what makes a gangster film, ranging from the simple inclusion of a villainous gangster in a film to those that follow outlaws on the run, the classic definition of a gangster film has revolved around the rise and inevitable fall of an immigrant gangster protagonist, a career criminal with whom audiences are expected to identify. Yet this classic definition has been expanded by evolving theoretical and methodological considerations of film genre. From the outset, gangster films were one of the first film genres to be considered by early genre criticism. Based on structural and formal analyses of the so-called Big Three Hollywood gangster films from the 1930s—namely, Little Caesar, The Public Enemy, and Scarface—early scholars argued that the gangster genre reflected the ideological tensions that underlay the American Dream of material success. Interpreted as modern tragic figures, gun-toting gangsters in these films were trapped in dangerous cities characterized by anonymity, violence, and death. More recently, genre criticism of gangster films has not only shifted emphasis away from the classic gangster narrative, but also paid far greater attention to institutional intertexts that have highly influenced the production and historical reception of these films. By highlighting variability, contingency, mutability, and flexibility, scholars now speak of genre in terms of specific cycles of production, where each cycle produces different gangster figures to mediate changing societal concerns and public discourses around issues of criminality, class, gender, and race. More contemporary and culturally-specific extensions, adaptations, or articulations of the gangster genre can also be read as thematic explorations of blackness in the following two ways. First, the cycle of ghetto-centric American “hood” films in the 1990s, a cycle that helped to launch hip-hop cinema, points to a continuity between the mythic figure of the gangster and African-American self-representations as “gangsta.” Secondly, while the gangster genre has been defined as a distinctly and explicitly American genre, owing much to critics’ primary emphasis on examining Hollywood films, the genre has also played a significant role in revitalizing and popularizing Hong Kong cinema in the late 1980s and 1990s. Referred to as hak bong dianying (“black gang films”), Cantonese-language Hong Kong gangster films are part of the fabric of local Hong Kong culture, revealing the moral implications of joining “black society” (the Cantonese-language concept for triad) and the “black paths” that members take.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".