Imagining a Non-Violent World "The Be the Peace, Make a Change Project": A Rural Community Peacebuilding Initiative to End Gender-Based Violence
Bibliographic record
Abstract
This article will profile the innovative community engagement process initiated by the "Be the Peace, Make a Change" project to end gender-based violence in Lunenburg County, Nova Scotia, and conclude with lessons learned. These lessons were summarized as "headlines" to imagine a future with new narratives for interpersonal relationships. This project was a three-year grassroots initiative of Second Story Women’s Centre, funded by Status of Women Canada. It engaged the rural communities of Lunenburg County to develop a coordinated response to violence against women and girls. It focused on the engagement of all genders, youth, and adults in exploring and implementing the visions, hopes and actions identified as priorities by the community within a peacebuilding framework. Community was broadly defined to include: survivors of relationship violence; professional service providers in healthcare, community services, policing and justice; municipal and provincial government; community-based services; educators and schools; clergy; and any interested citizens. The need to alter the cultural and social roots that sustain violence was recognized. A focus on building trusting partnerships both locally and provincially, inclusion of men and boys, engaging schools and youth and the justice systems, as well as survivors were hallmarks of the project.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".