Canadian Legal Problems Survey, 2021
Bibliographic record
Abstract
The Canadian Legal Problems Survey (CLPS) collects information on serious disputes or problems, which may or may not require legal help, Canadians have encountered and the impacts on their lives. Topics covered in the survey include the identification of the types of serious problems experienced, the relationship between those problems, actions taken to resolve or try to resolve the problems, access to legal help, costs associated with the legal problems, the level of understanding of the legal implications of the problems, the evolution and status of the problems, the impacts of the problems on their life including health, family and work. The survey aims to gather information that will help governments better understand the characteristics and mechanisms involved in those situations, evaluate Canadian's access to legal help and costs associated with legal issues. The survey results will inform the development of tools and measures to support Canadians experiencing legal issues and will be used in the evaluation of federal contributions to civil legal aid. In addition, the information will be used to inform and develop programs to address Canadians' legal needs and problems, such as supporting community justice centres, enhancing legal literacy and other people-centred approaches to access to justice.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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".