Reaching Out With Research: Engaging Community in Mapping Legal Service Accessibility, Effectiveness and Unmet Needs
Bibliographic record
Abstract
Emerging international research demonstrates that high economic and social costs accrue when individuals cannot access timely and effective resolutions to legal problems. Canadian research also shows that most people lack knowledge and understanding of legal rights, legal processes and services, and experience significant barriers when attempting to seek legal information and assistance. Within the Canadian justice community there is strong interest in engaging all relevant stakeholders in collaborative processes of research and policy development. This paper discusses how community-based mapping research can facilitate such engagement in compiling evidence that informs the development of legal processes and services that are more accessible, effective, efficient and fair. Community-based mapping research goes beyond recording details of geographical dispersement to include multiple perspectives on service accessibility, effectiveness and gaps within the context of local/regional social networks and relationships. Examples of Canadian projects are utilized to illustrate the application of this approach and its power to build both evidence and stakeholder networks. At the same time, the challenges of meeting all of the collaborative engagement and action for change goals are recognized.
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 | 0.067 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".