Desynchronized Infrastructures of Care: Suburban Imaginaries Re-Examined
Bibliographic record
Abstract
Introduction: Time and infrastructure in the sick suburbs This chapter examines the temporalities of infrastructure at the intersection of health, disease, and urbanization, especially peripheral, or extended, urbanization. At the start of 2020, we were set to study the life of people living with dementia (PLWD) in Toronto's immigrant suburbs. However, when the COVID-19 pandemic made that research temporarily impossible, we pivoted in our collaboration to the effect of COVID-19 on the urban peripheries of Toronto and Milan (Biglieri et al, 2020). This chapter draws from these two disparate research themes to present a rearticulation of our thinking around the ‘forever time’ that PLWD in suburbs may feel, and the presentism and ad hocism of the pandemic response. To make the connection between people experiencing the chronic, degenerative condition of dementia with those that suffered acute infection from COVID-19, we look at the suburban landscape of care in the Toronto region through a lens of infrastructural temporality. Our analysis engages the theoretically distinct but intrinsically linked perspectives of understanding infrastructure through a temporal analytic and seeing time itself as an infrastructure. Although neither approach is new per se, we argue that reading across them allows us to examine inequalities across both time and space and to understand the massive differences in how socially constructed times influence and condition the way infrastructures ‘ materialize ’ (Besedovsky et al, 2019; Coutard, this volume). In this chapter, we deploy a temporal analytic to examine how (sub)urban inequalities are created and perpetuated, especially for marginalized populations. Accentuating the temporal exposes structural inequities in regional infrastructures that are often hidden through performative and repetitive political discourses and processes that assign stereotypical roles and expectations to such places as inner cities and suburbs and the actors that inhabit them. Suburban landscapes, for instance, are often seen as lacking in ability to adapt to climate change and other systemic challenges but tend to remain both extensively burdened with infrastructure (from airports to waste dumps) and perpetually infrastructurally ill-equipped to deal with the growing ‘urban’ problematiques posed by suburban maturation and change.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".