Breathing life into theory: Illustrations of community-based research – Hallmarks, functions and phases
Bibliographic record
Abstract
There is a growing interest in the area of research that engages communities. Increasingly, this community-based research (CBR) approach to research is being seen as a catalyst for social innovation, for public policy improvements, for solving complex community issues, and for promoting democracy in which local knowledge is valued in building local solutions. This emerging interest in engaging communities in research (both within and outside academia) brings both successes and challenges.The purpose of this article is to summarise the theory underlying community-based research and to illustrate that theory with Canadian case examples of research studies conducted by the Centre for Community Based Research (CCBR). The article begins by reviewing the hallmarks, functions and implementation phases of community-based research, which are rooted in academic tradition. Three case examples are presented to illustrate the main hallmarks of CBR. The intention is to clarify community-based research by reflecting on iterative theory through practice and practice through theory.Keywords: community-based research, community-university research, knowledge production, knowledge mobilisation, community mobilisation, research for society
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".