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Record W4366959387 · doi:10.46692/9781529219067.021

The View from the Socio-Spatial Peripheries: Milan, Italy and Toronto, Canada

2021· other· en· W4366959387 on OpenAlexaboutno aff
Lorenzo De Vidovich, Julian Iacobelli, Samantha Biglieri, Roger Keil

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicUrbanism, Landscape, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyCartographyRegional scienceEconomic geography

Abstract

fetched live from OpenAlex

Introduction 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. How those factors add up across cities and urban networks specifically will be important to understand as this pandemic continues and as the urban world prepares to face the next, possibly still unknown, contagion that will pose a systemic threat. Put simply, the health crisis due to the COVID-19 pandemic is highly associated with processes of urbanization and globalization but in nonlinear and unpredictable ways. We follow the virus to the Italian metropolis of Milan and the Canadian urban region of Toronto. Milan was the European epicenter of the first wave, next to the Spanish capital of Madrid.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.107
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.000

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.

Opus teacher head0.010
GPT teacher head0.236
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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