Responding to Globalization and Urban Conflict: Human Rights City Initiatives
Bibliographic record
Abstract
Expanding globalization and urbanization have intensified the threats to human rights for many vulnerable groups and have restricted resources available to the primary guarantors of these rights—local authorities. Human rights cities initiatives are bottom-up efforts to advance human rights implementation in local contexts. They are emerging around the world in response to the global pressures on cities that intensify urban inequality and conflict. In this article I discuss how global changes are impacting cities and their abilities to protect the basic rights of residents. I then discuss the human rights cities model as a strategic response of social movements to secure people’s basic needs and strengthen local mechanisms for addressing social conflicts. I provide detailed analysis based on participatory research with Pittsburgh’s Human Rights City Alliance between 2013 and 2016. Drawing from literature on international peacebuilding, I argue that human rights cities are an emergent model of peacebuilding and governance that can guide policy and planning at multiple levels. Human rights movements are challenging neoliberal globalization’s emphasis on economic growth and putting forward frameworks that prioritize the needs of people and communities. In their appeals to international human rights norms, human rights cities advocates both advance international law and governance while giving voice to inherent contradictions between human rights and the policies of economic globalization.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.047 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".