Community Economic Development – A Viable Solution for COVID Recovery
Bibliographic record
Abstract
The COVID-19 pandemic has laid bare many of the weaknesses in our social and economic systems, exacerbating someof these challenges and drawing attention to others as we, collectively, find a way forward that results in a sustainable,inclusive, and equitable future for all. Around the world, community economic development (CED) initiatives already fosterinclusive economic revitalization, access to capital for business development, local ownership of resources, job creation,poverty reduction, and environmental stewardship. At a larger scale, CED can provide the foundation for COVID-19 recovery. This article outlines key policy proposals for CED-based recovery in Canada and elsewhere. Through the lens ofreconciliation with Indigenous Peoples, intersectionality, and a just transition to a low-carbon future, the CanadianCommunity Economic Development Network proposes the implementation of a national social innovation and social finance strategy and other complementary proposals for a post-COVID-19 world.La pandémie de COVID-19 a mis à nu plusieurs des faiblesses de nos systèmes sociaux et économiques, exacerbantcertains de ces défis et attirant l’attention sur d’autres alors que nous trouvons collectivement une façon d’aller de l’avantqui mènera vers un avenir viable, inclusif et équitable pour tous et toutes. Partout dans le monde, les initiatives dedéveloppement économique communautaire (DÉC) favorisent déjà la revitalisation économique inclusive, l’accès auxcapitaux pour le développement d’entreprise, la propriété locale des ressources, la création d’emploi, la réduction de lapauvreté et l’intendance environnementale. À une plus grande échelle, le DÉC peut fournir la fondation pour la relancesuite à la COVID-19. Ce document présente des principales recommandations de politiques pour la relance basée surle DEC au Canada et ailleurs. En tenant compte de trois exigences—la réconciliation avec les peuples autochtones,l’intersectionnalité et une transition équitable vers un avenir à faible émission de carbone, le Réseau canadien dedéveloppement économique communautaire propose la mise en oeuvre d’une stratégie nationale d’innovation sociale etde financement social et d’autres propositions complémentaires pour un monde post-COVID-19.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".