Bibliographic record
Abstract
It is well understood that capitalist systems maintained by cities result in unequal distribution of economic growth, resources, and opportunities. One central dynamic contributing to these socio-spatial inequalities stems from asymmetrically distributed resources for care. Caring is a fundamental human activity that involves an attentiveness to the needs, vulnerabilities, and well-being of others. However, in many cities today, particularly in North America, political ideologies understand care as individual responsibility and achievement. Yet, at the same time, cities are also repositories that generate resistance toward inequality. In other words, metropolises are beginning to factor in new ways to make care possible. This paper therefore asks: how is care, in all its forms, made possible by cities? To answer this question, it explores a city's capacity to care in ways that include but also exceed social and welfare policies. This is achieved by examining the development and operation of a pilot food incubator program in Toronto. In particular, it employs community engaged research and interview strategies to make sense of the power relations between the program actors through a ‘caring with’ lens. Engaging such strategies while focusing on care reveals novel municipal governance perspectives on the one hand. And on the other it offers practical implications by illustrating the program's efficacy in accomplishing its goals. Making sense of the relationship between metropolises and care, this paper argues that cities ought to be judged not on how economically competitive they are, but on how they best foster care for people and future generations.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".