Governing infectious disease in the urban periphery: marginality, informality and vulnerability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper works toward building a theoretical framework to understand the role that extended urbanisation and peripherality played in the COVID-19 pandemic, with a specific focus on conceptualising and analyzing the nature of the social impacts and outbreak responses that unfolded in urban peripheries. In particular, we emphasise how scholarship on socio-spatial peripheralisation—as part of broader approaches devoted to analyzing the nature of extended urbanisation more generally—may vitally inform current discussions about the ways the urban periphery continues to be defined and debated in the wake of the pandemic. We make the case that governance of urban society must accept and respond to the territorial and scalar perforations, multiple diversities and deepening inequities that were highlighted in the pandemic. The chief contribution of this article is that the ways the urban periphery has been defined and debated has been associated with the changing political ecologies of urbanisation. To analytically explore this relationship we deploy and extend relevent concepts like extended urbanisation and suburbanisation, peripherality, marginality, and informality. Our intervention in the distinct but related debates on extended urbanisation and peripheralization adds a further dimension to consider in the governance of disease and cities.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it