Class Actions to Remedy Mass Consumer Wrongs: Repugnant Solution or Controllable Genie? The Canadian Experience
Bibliographic record
Abstract
From a Canadian perspective, by far the most important and pressing problem facing postindustrial societies is not the adoption of more consumer legislation but the non-or negligible enforcement of existing laws.The problem is bad enough when it is experienced by consumers with individual grievances, but it grows exponentially when the wrong affects not just a handful of consumers but thousands of consumers.Drawing on the Canadian experience, examples abound all around us: false advertising, collusive price fixing, harmful drugs and therapeutic devices, usurious interest rates, unlawful banking charges, "vanishing premiums" in life insurance contracts, inflated prices for automobile repairs, and other consumer services. 1I. GOVERNMENTS AS PART OF THE PROBLEM, NOT THE SOLUTIONIn Canada (and the same is surely true of other countries in the Western hemisphere), there is no shortage of federal and provincial consumer legislation.Much of it has been adopted over the past forty years.Only rarely is the legislation accompanied by machinery for its effective enforcement.Between 1960 and 1980, the federal government in Canada and many of its provinces established new ministries and new
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.028 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".