Embracing emergence: reframing and reimagining the visionary communities project
Bibliographic record
Abstract
Addressing complex sustainability challenges has encouraged a growing interest in community-university partnerships and collaboration. Transdisciplinary knowledge co-production (TKC), a form of practice-oriented research, is committed to embedding pluralistic forms of knowledge into various aspects and stages of research. This paper explores how a TKC orientation, and a combination of academic and practitioner ideas, knowledge and experience, shaped the governance and research design of transdisciplinary knowledge co-production research project, Visionary Communities, embedded in a marginalised neighbourhood in Toronto, Canada. The first stage of the project involved 8 months of aligning academic and community perspectives to address governance and role issues, determining rules of engagement, a theory of change and a combined set of objectives and engagement principles. Academic researchers’ ideas of sustainability as an emergent property of discussions of desired futures (procedural sustainability), generative of net positive outcomes (regenerative sustainability) and embedded in social and institutional practices (normalising sustainability) contributed conceptual underpinnings of the project. Equally, the project’s community partners’ experience working in and with community contributed a framework for understanding communities as a relational ecosystem of collective assets, formalised as the Connected Community Approach. Weaving academic and practitioner approaches together led to four major reframing and reimagining moments of the Visionary Communities project: finding and sharing resources, project development, combining theoretical frameworks and making climate change/sustainability co-benefits.
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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 | 0.039 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.045 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".