Toronto's Quiet Crisis: The case for Social and Community Infrastructure Investment
Bibliographic record
Abstract
In the debate over the future of cities in general and the future of Toronto in particular, attention has so far focused on the crisis in physical infrastructure, including the need to improve public transit, build affordable housing, and keep roads and sewers in good repair. But equally important is the state of a city's social and community infrastructure - including child care, public libraries, neighbourhood centres, old age homes, public health units, environmental protection initiatives, settlement support for immigrants and refugees, and recreation programs. These programs benefit families, help vulnerable individuals, build skills and community capacity, and contribute to the quality of life for all community members. This vital but often-over1ooked part of Toronto's infrastructure is struggling to survive in the face of budget cutbacks, which have led to understaffing, higher fees, long waiting lists, the elimination or reduction of programs, and the persistence of unequal levels of service in different parts of the City. If the City succeeds in securing new financial arrangements with senior levels of government to pay for physical infrastructure, it must commit to redirecting revenues from property taxes to restore social and community infrastructure. Those funds could be used to eliminate waiting lists and staff shortages, do away with user fees, restore cuts to essential services, and provide new or expanded programs required to ensure equitable access for all Toronto communities. At the same time, senior levels of government should share the costs of repairing Toronto's fraying social and community infrastructure or allow the city new tax revenues to support social infrastructure. This paper estimates the costs of meeting pressing needs in selected areas of Toronto's social infrastructure, including children's services, parks and recreation, public health, public libraries, environmental protection, public shelters, and program grants to not-for-profit community agencies.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".