Indicators of peace: in Winnipeg, Manitoba, Canada “it depends who you are:” an exploratory case study of the saliency of quantitative positive peace measures
Bibliographic record
Abstract
Winnipeg is a city regarded in Canada as the city in the middle. It is, in fact, geographically at the heart of the North American Continent. It is also situated currently as a city of prevalent racism and injustice. It is also a city where there is consistent and prevalent peace work as well. It would seem Winnipeg is a city divided. Winnipeg served for thousands of years as a place for Indigenous Nations to gather, trade, and share culture. The decades and centuries that followed first contact replaced these systems with infrastructures and cultures of oppression and violence, exacting painful tolls on Indigenous Peoples and people of colour more generally. Over the last century, however, there have been social, economic, and political shifts in the city toward accepting and embracing the cultural differences it once demonized, effectively beginning the process of erecting shared cultures of peace. The purpose of the exploratory research in this study is to gauge the relative levels of positive peace or social justice in Winnipeg. To accomplish this, I drew four salient quantitative indicators from the proventive critical positive peace assessment known as the peace poles, to test their viability. To serve as a counterweight and balance to test of the measure’s saliency, representatives from four Winnipeg social organizations were undertaken to explore their ideas about positive peace in Winnipeg as the city in the middle. These four organizational representatives come from a few of the most systemically engaged organizations in the city. In this research I explored the ways in which these persons and organizations engaged in the city regarding peace, while afterward running the numbers derived from the peace poles assessment published in the Palgrave Handbook of Positive Peace in 2022. These positive peace metrics—connectivity, political voice, the gini index, and sense of belonging—only further grounded and supported the themes discovered in the interviews.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".