The Ecology of Place-Based Impact Investing Ecosystems and Enabling Environments in Non-Metro Canada.
Bibliographic record
Abstract
Place-based impact investing ecosystems can be described as complex living systems. This research focuses on examining place-based impact investing ecosystems and enabling environments in non-metro Canada. A model emerged from this research called the Place-Based Impact Investing Ecosystem and Enabling Environment (PIIE) model. The intent for PIIE is to provide a holistic understanding of the ecology of these systems, capturing multi-level interactions within complex systems where place-based impact investing ecosystems and enabling environments exist. The PIIE model consists of a macro, meso, and micro level, and addresses gaps in the literature. PIIE positions a macro-level understanding of the influence of dominant political ideologies on policy development, global markets on investment and the influences of globalization on (national, provincial, and municipal) economies, and sustainable development's role in the broader development of impact investing by fostering social innovation. The meso-level provides an opportunity to analyze pressures created by political ideologies on policy development, and its influence on enabling environments, the economies where place-based impact investing and ecosystem development exist, and the social innovation that cultivates the development of tools used to address complex challenges. The micro-level situates the three main components essential for place-based impact investing ecosystem development. Place is at the centre of PIIE, illustrating the most essential component- the intent to create an impact in the communities they serve.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".