Bibliographic record
Abstract
The regulation of payday loans holds the potential of extending the benefits of regulating overindebtedness, currently provided via bankruptcy legislation to the middle-class, to lower income debtors. This potential needs to be balanced against lower income debtors' need for credit and the corresponding benefits resulting from access to credit provided by alternative credit markets, such as the payday lending market. Unlike the United States, where payday lenders have more locations than Starbucks and McDonalds combined, and payday lending regulation is up there with Vampire Weekend and the Tipping Point as an attention grabbing pop-culture reference, payday lending is relatively new, underdeveloped and unregulated in Canada. Over the last year, in the wake of a recent amendment to the Canadian Criminal Code, that would see payday lenders exempted from the 60 per cent criminal rate of interest in provinces where payday lenders are provincially regulated, Canadian provinces have began to regulate and put forth regulatory proposals for a previously unregulated area. This exercise has been attempted in the context of limited recent domestic analysis of the payday lending industry, borrowers and regulatory options. Accordingly, this article sets out to fill this void. The article draws on the American experience with payday lending and payday lending regulation, and also a first-hand experience of attempting to obtain a payday loan in Toronto, Ontario, to evaluate the current provincial reform efforts.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.012 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| 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".