Mobilizing knowledge about urban change for equity and sustainability: developing ‘Change Stories’, a multi-country transdisciplinary study
Bibliographic record
Abstract
Background: Health-focused research funders increasingly support multi-country research partnerships that study health, urban development and equity in global settings. To develop new knowledge that benefits society, these grants require researchers to integrate diverse knowledges and data, and to manage research-related aspects of coloniality, such as power imbalances and epistemic injustices. We conducted research to develop a transdisciplinary study proposal with partners in multiple middle and high income countries, aiming to embed equity into the methodology and funding model. Methods: Parallel to literature review, we used participatory and social research methods to identify case study cities for our primary study and to inform our study design. We conducted semi-structured interviews with informed and consented sustainable urban development experts in the USA (n=23). We co-developed our research approach with our global advisory group (n=14) and conducted a participatory workshop (n=30) to identify case study sites, also informed by conversations with international academic experts in sustainable development (n=27). Results: Through literature review we found that there is a need to study the contextual pre-conditions of urban transformation, the influence of coloniality on understandings of how cities can change and the failure of standard development practices to meet the needs of all residents and the planet. Through expert input and literature we found that decolonial and storytelling methods may help us show the complexities behind stories of urban transformation, particularly the role of marginalized populations in creating long-term change. Conclusions: There are multiple benefits of conducting research to develop an equitably designed multi-country research collaboration. We built new partnerships and co-developed our research approach, creating new understanding of diverse collaborators' disciplinary perspectives and institutional requirements. By investigating the informational needs of U.S. sustainable development actors and designing our study to meet these needs, we have increased the likelihood that our research will create impact.
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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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".