Five urban health research traditions: A meta-narrative review
Bibliographic record
Abstract
Urban health scholars explore the connection between the urban space and health through ontological perspectives that are shaped by their disciplinary traditions. Without explicit recognition of the different approaches, there are barriers to collaboration. This paper maps the terrain of the urban health scholarship to identify key urban health research traditions; and to articulate the main features distinguishing these different traditions. We apply a meta-narrative review guided by a bibliometric co-citation network analysis to the body of research on urban health retrieved from the Web of Science Core Collection. Five urban health research traditions were identified: (1) sustainable urban development, (2) urban ecosystem services, (3) urban resilience, (4) healthy urban planning, and (5) urban green spaces. Each research tradition has a different conceptual and thematic perspective to addressing urban health. These include perspectives on the scale of the urban health issue of interest, and on the conceptualisation of the urban context and health. Additionally, we developed a framework to allow for better differentiation between the differing research traditions based on (1) perspectives of the urban system as complicated or complex, (2) the preferred locus of change as a function of structure and agency and (3) the geographic scale of the urban health issue that is addressed. These dimensions have even deeper implications for transdisciplinary collaboration as they are underpinned by paradigmatic differences, rather than disciplinary differences. We conclude that it is essential for urban health researchers to reflect on the different urban health approaches and seek coherence by understanding their similarities and differences. Such endeavours are required to produce and interpret transdisciplinary knowledge for the goal of improving health by transforming urban systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.034 | 0.027 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".