Preface to All Four Volumes of Global Reflections on COVID-19 and Urban Inequalities
Bibliographic record
Abstract
You are currently reading one of the four volumes of Global Reflections on COVID-19 and Urban Inequalities, which jointly explore schisms the pandemic has both revealed and widened, and measures taken to mitigate or eradicate these societal gaps. The aim of this series of edited volumes is to bring together a collection of critical urban voices across various disciplines, geographies, and perspectives in order to examine the urban challenges of COVID-19 and its impact on new and existing inequities in cities around the world. There are two sides to the pandemic. As a highly contagious disease, given enough time and a lack of effective mitigation to restrain its spread, COVID-19 will eventually infect a large majority of the population, regardless of income or geography. This is why many public health measures are directed at entire national (or indeed global) populations. But we have also quickly learned that COVID-19 is selective in its effects – for instance, based on age and comorbidity – and that the pandemic and responses to it exacerbate fault lines traversing cities, societies, and, indeed, the world order. There is a clear urban dimension to these inequities. Some parts of the city and some populations who reside in cities are more likely to contract and spread the virus. COVID-19 is thus an amplifier of pre-existing social divisions. Access to medical treatment and possibilities to physically isolate from potential infection are unevenly distributed. So too are the consequences of policy responses, such as lockdowns, the economic impacts of the pandemic, and the individual and political reactions it prompts. The pandemic has therefore increased divisions such as between young and old, rich and poor, left and right, and countless other societal dichotomies. As a result, experiences of urban life during the pandemic vary greatly. Where these impacts of the pandemic intersect with pre-existing racism, ageism, sexism, ableism, and spatial divisions within the city, the consequences have been particularly severe. As we write this preface, vaccines are starting to be produced, distributed, and administered. This poses new questions: will we emerge from the pandemic thanks to these vaccines? How equitable will the distribution of vaccines be within countries and at the global scale? This context suggests myriad potential urban futures. The planning, policy, and political choices made in the short term will impact the medium-and long-term trajectories of cities and the lives of their residents.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.077 | 0.019 |
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".