The Needs of Helping Organizations in the Community
Bibliographic record
Abstract
In access to justice, needs are ordinarily conceived in terms of individuals experiencing legal problems requiring assistance from someone with expertise and resources to resolve that problem. Legal problems studies have pointed out the vast number of problems with possible legal aspects experienced by members of the public. In Canada, repeated national surveys have estimated that about half of all adult Canadians will experience one or more problems within a three-year period. This amounted to more than 11 million people estimated by the most recent Canadian study and a greater number of problems because some people experience multiple problems.1 This volume of need would overwhelm conventional legal services providers who embrace the notional goal of meeting the needs of the public. However, there are many examples of how access to justice can be extended toward meeting the needs of the public by partnering with community organizations that already assist people with problems. Developing successful collaborative partnerships between legal clinics and community-based helping organizations requires the recognition that these organizations have needs as do the individuals they assist. Meeting these needs is integral to expanding access to justice. Two kinds of needs are discussed in this paper; 1) needs related to assisting helping organizations better serve their own clients and 2) needs that arise from the collaborative partnership between legal service providers and helping organizations itself.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.002 |
| 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".