Bibliographic record
Abstract
This article highlights the class action proceeding in Quebec, seeking to expose its main characteristics. The article initially explores the Canadian legal system and provincial legislative peculiarities. In addition to that, the paper explores the three major objectives of class actions: (i) judicial economy; (ii) maximizing access to justice; (iii) deter actual and potential wrongdoers from inflicting damage, especially small amounts of damage on a larger number of people (preventative objective). In sequence, such article specifies how the two-step class action procedure works, starting with the application for authorization, which is a preliminary request and a unique step in the province of Quebec that is meant to filter frivolous demands.Only once the authorization is granted may the case be heard collectively on the merits. The second step of the two-step procedure is an originating application that must be filed if the class action is authorized. Furthermore, this study deals with many relevant matters regarding the class actions in Canada (particularly Quebec), such as: (i) Right of Appeal; (ii) Res Judicata Effect – on absent members; (iii) Monetary Distributions and Types of Collective Recovery; (iv) Class Action Financing; (v) Possible Settlements; (vi) Multi-Provincial Class Proceedings and National Classes.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".