The Role of Governments in the Overindebtedness of the Economically Disadvantaged
Bibliographic record
Abstract
In this article, Professors Stephanie Ben-Ishai and Saul Schwartz examine several ways in which the government becomes a creditor of economically disadvantaged Canadians and its role in limiting the options available for resolving the resulting overindebtedness. Specifically, the authors explore how government transfer programs, and the debts that result from benefit overpayment, affect those already marginalized by poverty. Ben-Ishai and Schwartz then argue that the two main remedies available to Canadians facing insolvency - credit counselling and bankruptcy - are simply too costly for low-income individuals, a term the authors use to describe those who are poor even before they incur the serious debts leading up to an insolvency. Low-income Canadians coping with government debt are shown to be in a unique and difficult position with respect to repayment. Using the overpayments that can occur within transfer programs such as Ontario Works (OW) and the Ontario Disability Support Program (OSDP) to illustrate the particular issues affecting low-income individuals, the authors demonstrate the lack of recourse this group has when dealing with insolvency. Ben-Ishai and Schwartz analyze this issue using the statutory framework, interviews with government program officials and data on social assistance overpayments to cast doubt on the assumption that those with low income have no need for bankruptcy and credit counselling. In so doing, the authors ultimately question whether existing insolvency remedies are serving the needs of all Canadians.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.024 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".