Bibliographic record
Abstract
In this paper, I will address the value of the class action procedure, both theoretically and empirically. It will attempt to clarify Canada’s supposed “littlest secret”, which is that while class actions represent a significant part of our court activities, they may not truly be compensating our citizens. We are witnessing a “wealth” of class action trials being held around the country on an annual basis, with the largest number of trials occurring in the province of Quebec, followed by Ontario, and subsequently, British Columbia. I will argue that leading up to the present study, we did not know for certain whether a class action was an effective mechanism to compensate class members. Through empirical data collected up until recent years from cases filed in the province of Quebec, analysed through the lens of a collective approach to compensation, I will demonstrate that Quebec citizens are in fact compensated through the use of class actions.
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 | 0.029 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".