The Ecology of Place-Based Impact Investing: Examining Place-Based Impact Investing Ecosystems and Enabling Environments in Non-Metro Canada
Bibliographic record
Abstract
Non-metro Canada is under tremendous pressure, navigating a global pandemic, rising cost of living, inflation, a collapsing healthcare system, skilled labour and housing shortages, supply chain issues, and the impacts of climate change. The current economic, business, and finance systems are built on capital accumulation. These systems perpetuate exploitative and extractive markets that deepen inequity, systemic racism, environmental degradation, social and economic injustice. Place-based approaches provide context for complex and interconnecting issues at a local level; by providing a place-based lens, interventions can be targeted, leveraging investment capital to create impact outcomes for the communities they serve. Place-based approaches also leverage community assets, connecting networks, skills, knowledge, and opportunities for co-creation and collaboration. However, critical to the viability, depth of impact, and sustainability of place-based investment are supportive ecosystems. Place-based impact investing ecosystems are complex, living systems that can be responsive to and influence the environments in which they exist. Since the 1980s, neoliberal economic restructuring, devolution of programs and services, and precarious funding and financing has eroded non-metro resilience. This has directly impacted the ability for non-metro communities to have robust and healthy enabling environments for place-based impact investing ecosystems. This research uses a grounded constructivist methodology to examine place-based impact investing ecosystems and enabling environments in non-metro Canada. The contributions of this work address gaps in literature on the understanding of place-based impact investing ecosystems, the composition, and the enabling forces in non-metro Canada. This research also contributes to understanding the opportunities and limitations of current policy, and the role that coherent policy has in developing enabling environments. Place-based impact investing ecosystems can be described as a complex living system. A model examining the ecology of place-based impact investing ecosystems emerged from this research, and contributes to understanding multi-level macro, meso, and micro influencing factors of place-based impact investing ecosystems and enabling environment development. The place-based impact investing ecosystem and enabling environment (PIIE) model contributes to the field by situating a macro-level understanding of the influence of dominant political ideologies on policy development, global markets on investment, the influences of globalization on (national, provincial, and municipal) economies, and the role that sustainable development has in fostering the broader development of impact investing through social innovation. The meso-level provides an opportunity to analyze pressures created by political ideologies on policy development, including influence on the development of enabling environments, the economies where place-based impact investing and ecosystem development exist, and the social innovation that cultivates 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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".