Community-Based Justice Research (CBJR) Project: Exploring Community-Based Services, Costs and Benefits for People-Centered Justice
Bibliographic record
Abstract
The CBJR Project is a collaborative international initiative featuring exciting new research exploring the costs and benefits of community-based justice. The CBJR Project partners include the Katiba Institute in Kenya, the Center for Alternative Policy Research & Innovation in Sierra Leone and the Centre for Community Justice & Development in South Africa, with collaboration and support from the Canadian Forum on Civil Justice and the International Development Research Centre.\nSince 2018, the CBJR Project partners have been working to learn more about the benefits, costs and opportunities of providing and scaling various community-based justice services and initiatives, as well as the benefits, opportunities and challenges of collaborating on a multi-country empirical access to justice research initiative.\nThe deliverables of the CBJR Project include not only specific research outputs, but equally if perhaps not more importantly an attention to relationships and processes. Specifically, the CBJR Project partners collaborated on several shared goals, including goals focussing on the development of research partnerships, peer-to-peer support, methodological innovation, community-based justice research and data collection, scaling local initiatives as part of a global access to justice agenda, adding to a shared justice culture shift, and – as the project developed – understanding how to conduct research in the context of a COVID-19 world.\nThis final report, which builds on companion publications and earlier reports, is the final report of the CBJR Project. The purpose of this final report is not to summarize all of the work and findings of the three African-based CBJR country projects, which are well-documented and speak for themselves in their various final reports. Rather, the purpose of this final report is to briefly describe and summarize the overall CBJR Project, to situate the CBJR Project in the context of the global access to justice crisis, to identify and briefly discuss some shared findings (primarily from the African-based CBJR Project studies as well as, very briefly, some recent Canadian initiatives) and, overall, to learn from the work of our African colleagues in the context of the growing body of scholarship on access to justice generally and community-based justice in particular, all within the growing body of international people-centered access to justice research and reform efforts.
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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 | 0.038 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".