Why We Love to Hate the Wolf (of Wall Street): Using Georges Bataille and Friedrich Nietzsche to Critique the Function of Moral Ideology Under Late Capitalism
Bibliographic record
Abstract
Abstract The release of Martin Scorsese’s film The Wolf of Wall Street in late 2013 helped to reignite a public conversation about corporate greed and the moral excesses and violations of Wall Street firms and executives. A barrage of articles, reviews, and criticisms of the film emerged throughout popular media that sought, for the most part, to single out and condemn the immoral actions and behaviors of individuals (for example, Jordan Belfort, whose actions constitute the primary subject matter of the film) within a pre-given and non-negotiable context of capitalist economic and social relations. This article uses the writings of Georges Bataille and Friedrich Nietzsche to critique this popular discourse. It reads the discourse as structured by a false identity of opposites, whereby the normal, moral, legal, and “peaceful” state of things is depicted as constitutive of a separate world from that of Belfort and the “criminal” excesses and expenditures of Wall Street. As a result of this conceptual maneuver, a mode of moralizing is enabled. In a fit of ressentiment, critics unleash their moralizing sentiments, single out and constitute guilty subjects, and hold these subjects responsible in order to repair the “secondary malfunctions” of capitalism. They do this so that capitalism can continue to survive and so they can have a good conscience while it does.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".