Beyond the Figure of the Problem Gambler: Locating Race and Sovereignty Struggles in Everyday Cultural Spaces of Gambling
Bibliographic record
Abstract
As gambling has become a ubiquitous feature of many neoliberal capitalist societies, the problem gambler has become a familiar cultural figure, invoked in regulation, popular culture and everyday life. This article brings critical research on governmentality together with cultural studies and critical Indigenous scholarship on whiteness, race and sovereignty to understand the racial biopolitics of gambling beyond the individual subject of problem gambling. I argue that, for settler-colonial states, gambling plays a role in maintaining tropes of cultural representation and securing legal and political power within an overarching system of white racial entitlement. An investigation of cultural spaces and products of gambling in Australia, together with close readings of Indigenous creative works, ties the figure of the problem gambler to broader processes of what Goldberg calls ‘racial neoliberalism’. I show how this figure becomes a metonym for dysfunctional consumption, is harnessed to racially targeted welfare reforms, and used to undermine the rights of Indigenous people, both as gamblers and as sovereign political and legal subjects.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.068 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".