Community Bonds and Canada’s Foundations: Rethinking Risk and Financial Outcomes
Bibliographic record
Abstract
Community wealth building (CWB) offers a place-based approach to impact investing, fostering local economic development and wealth retention (Dowin Kennedy, 2021; Guinan & O’Neill, 2019; O’Neill & Howard, 2018; Ratner, 2019). Community bonds (CBs), a CWB tool, challenge traditional wealth models but remain underutilized due to limited awareness (Surman & Hughes, 2012; Hughes, 2013). This study examines risk perceptions versus financial performance in the Canadian CB market, arguing that addressing information asymmetry is key to unlocking capital and scaling impact. Using historical repayment data, it introduces a dataset of CB offerings and proposes a bond rating system to reduce risk. The study also develops an investor typology and explores partnerships among investors, issuers, and intermediaries through a CWB lens.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".