Bibliographic record
Abstract
This article explores the development of legal aid services in Ontario over the past two decades. The authors find that per capita inflation-adjusted spending on legal aid services by the federal government has been in long-term decline (albeit with periodic upturns) with resulting negative impacts on access to justice for those in need of legal assistance. At the provincial level, since cuts made in the mid-1990s, financial eligibility guidelines have remained out of line with real measures of poverty, such as Statistics Canada’s low-income cut-offs, and per capita funding has only recently increased. The mix of legal aid service providers in the province consists of fewer certificate lawyers and per-diem duty counsel than in the past. The recent introduction of new service providers and technological innovations—driven by a desire to both reduce costs and improve client services—may have produced some positive outcomes, however, research has not yet established whether new service providers and new technologies are simply backstopping, rather than augmenting, prior levels of service. The authors conclude that there is a need for more research on the developments they describe and on how legal aid services can be enhanced and expanded in the future.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".