Investing in Place: How nonprofits, foundations, and local government make social investments to affect change in community economic development in Toronto's Neighbourhoods Improvements Areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In response to growing economic disparity in the City of Toronto, there has been a shift towards a community economic development approach in Toronto's most disadvantaged neighbourhoods, exemplified by the Toronto Strong Neighbourhoods Strategy (2005) and the Toronto Strong Neighbourhoods Strategy 2020. In addition to the City's efforts, a small community of foundations and nonprofits are also taking this approach. While community economic development has shifted the focus and social investment practices of these organizations, its impact is less clear. In this paper, I explore how nonprofits, foundations, and local government make decisions about how to make social investments to affect change in community economic development in Toronto's Neighbourhoods Improvements Areas (NIAs). On the basis of this analysis, I offer a series of recommendations to enhance decision making processes and evaluate the impact of these social investments on local economies in Toronto's NIAs.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 it