MétaCan
Menu
Back to cohort

The US Debtfare State and the Credit Card Industry: Forging Spaces of Dispossession

2012· article· en· W2089869615 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAntipode · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsQueen's University
Fundersnot available
KeywordsCapitalismCredit cardDebtEconomicsState (computer science)IdeologyMaterialismPopulationMarket economyPolitical economyFinanceSociologyLawPaymentPolitical science

Abstract

fetched live from OpenAlex

Abstract: Credit card debt is a ubiquitous feature of neoliberal capitalism. To explain the notable growth of credit card usage in the US, I adopt a historical materialist approach that employs two key analytical concepts—cannibalistic capitalism and the debtfare state—to capture the material, institutional and ideological dimensions of this process. Viewed within the bounds of cannibalistic capitalism, a mode of accumulation primarily based on the expansion of fictitious capital and secondary forms of exploitation, the debtfare state enhances the social power of money by allowing major credit card issuers (banks) to generate high levels of income from uncapped interest rates and policies that ensure the extension of plastic money to those who fall within Marx's category of the surplus population. While the expansion of debt subjects surplus workers to the disciplinary requirements of the market, it is unable to suspend the main tensions of cannibalistic capitalism, prompting ongoing reconstructions of the debtfare state.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.230
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it