Innovations from the Margins: The Community Ideas Factory community collaboration
Bibliographic record
Abstract
This paper explores the importance of building more inclusive, equitable, and mutually-beneficial partnerships in academic-community research collaborations for social innovation. The Community Ideas Factory is a research project that examines food security, affordable housing, employment equity and wrap-around services in the Region of Halton in Ontario, Canada. The project is a unique and dynamic collaboration between researchers from Sheridan College and the Oakville Community Foundation. In recognizing the limitations of traditional, paternalistic, subjective academic-community research collaborations this paper discusses how Participatory Rural Appraisal tools and other community-based problem-solving activities can be used to help communities define and prioritize their own problems, identify resources, and develop practical solutions to the problems they experience. We seek to demonstrate the potential of a new role for the ‘researcher’; one in which she/he assumes a more active and dynamic, yet facilitative, role in community project-building. Drawing examples from our research into food security this examination aims to provide insights, directions, and considerations for scholars, community stakeholders, and granting agencies alike who share an interest in the prospects and possibilities of academic-community collaborations for social innovation research.
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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 | 0.049 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.047 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.003 | 0.044 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".