In the Shadow of the Welfare State: The Role of Payday Lending in Poverty Survival in Australia
Bibliographic record
Abstract
Abstract A defining characteristic of contemporary welfare governance in many western countries has been a reduced role for governments in direct provision of welfare, including housing, education, health and income support. One of the unintended consequences of devolutionary trends in social welfare is the development of a ‘shadow welfare state’ (Fairbanks, 2009; Gottschalk, 2000), which is a term used to describe the complex partnerships between state-based social protection, voluntarism and marketised forms of welfare. Coupled with this development, conditional workfare schemes in countries such as the United States, Canada, the UK and Australia are pushing more people into informal and semi-formal means of poverty survival (Karger, 2005). These transformations are actively reshaping welfare subjectivities and the role of the state in urban governance. Like other countries such as the US, Canada and the UK, the fringe lending sector in Australia has experienced considerable growth over the last decade. Large numbers of people on low incomes in Australia are turning to non-mainstream financial services, such as payday lenders, for the provision of credit to make ends meet. In this paper, we argue that the use of fringe lenders by people on low incomes reveals important theoretical and practical insights into the relationship between the mixed economy of welfare and the mixed economy of credit in poverty survival.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".