The View from the Socio-Spatial Peripheries: Milan, Italy and Toronto, Canada
Bibliographic record
Abstract
At first, the virus causing COVID-19 spread from Wuhan in much the same way as its predecessor, Severe Acute Respiratory Syndrome (SARS), did in 2003: through the network of global industrial and financial centers that define the structure of the world economy. But the trajectory of COVID-19 turned out to be more complex: the new virus proliferates nearly everywhere, including in urban peripheries that have characterized recent urbanization trends. The environments of COVID-19 transmission in the global urban peripheries coalesce into multifaceted and complex geographies characterized by health care system (in)equality, lack of infrastructures, overcrowding, low-wage labor, racism, vulnerability of age/living in an institution, and so on. While posing a major challenge to public health systems around the world, the pandemic has thrown the contemporary challenges for the responses to outbreaks of emerging infectious diseases into sharper view, especially with reference to the accelerated extension of urban processes and forms into regions that had previously not been urbanized. But twelve months into the pandemic, as it has now rolled over most settled regions around the world, places in the global urban world and the spaces between them have generated complex, and often contradictory, outbreak and reopening narratives. Urbanists have weighed residential density, degrees of informality, transportation modes, housing form, availability of park space, and a host of other factors in determining patterns in the proliferation of COVID-19. Social scientists have pointed to race, class, age, disability, and gender as important determinants. Students of public policy and institutions have pointed to insufficiencies in public health pandemic preparedness and catastrophic negligence in long-term care homes. Labor researchers have highlighted the lack of state regulation and oversight at precarious workplaces such as meatpacking plants, in the agricultural sector, and in long-term care homes.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".