‘TO HEAP DISTRESS UPON DISTRESS?’ COMPARATIVE REFLECTIONS ON INTEREST-RATE CEILINGS
Bibliographic record
Abstract
Interest-rate ceilings are often proposed as a protection for lower-income consumers in the credit market. Economists are generally sceptical of the protective role of ceilings, arguing that they often have undesirable substitution and exclusionary effects, may be circumvented, and hurt most those whom they are intended to protect. More competition, better information, and financial literacy are proposed as alternative policies, together with more effective redistribution through the social system. This was the conclusion of David Cayne and Michael Trebilcock in 1973 in their examination of the problem that ‘the poor pay more.’ Notwithstanding these economic critiques of ceilings, many European countries retain ceilings, and Japan recently lowered its existing ceiling. There does not seem to be an ‘end of history’ as nations converge on a ‘modern’ understanding of ceilings. This article sketches recent debates in the United Kingdom (no ceilings) and France (ceilings) where both countries view their policies as protection against financial exclusion. The author outlines the role of empirical knowledge and the value assumptions in these debates, raises the question of whether the differences represent distinct national cultural preferences, and suggests that explanations of consumer credit regulation should be sought in the dynamics of political interest-group influence and its institutional setting in both countries.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".