COMMUNITY ECONOMIC DEVELOPMENT: A FORCE FOR NEIGHBOURHOOD RESILIENCE
Bibliographic record
Abstract
Despite long-standing, complex challenges facing Winnipeg including poverty and social exclusion, communities within this city are creating multifaceted, innovative, and holistic solutions. This is often understood as community economic development (CED). This approach can be difficult to define and includes a multitude of examples, each with very different characteristics. This is primarily because the approach focuses on community-leadership and local development, resulting in models that are tailored to the unique characteristics of each community. \n \nIn Winnipeg, there have been three evolutions in CED over the past twenty years. First, there was a coalescence around this approach with a number of key organizations created explicitly using CED as their guiding methodology. After that, place-based development where CED principles were put into action to revitalise struggling neighbourhoods emerged. More recently, social enterprise is developing as a model with significant promise for creating healthy, community-owned, local businesses and good jobs for people who struggle to gain employment. \nThis article details this development, the political environment that has either restricted or enabled this approach, and some key organizations utilising community economic development in Winnipeg.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".