Community Ecosystem Mapping: A Foundational Step for Effective Community Engagement in Research and Knowledge Mobilization
Bibliographic record
Abstract
Community engagement is a key strategy for achieving various goals, such as social and environmental change, sustainable development, health promotion, and community building. It involves collaborations and partnerships with the community that help mobilize resources, impact systems, rectify partner dynamics, and function as catalysts for modifying policies, programs, and practices. It also ensures mutual trust among all parties involved, giving community members greater personal agency and involvement potential. We have learned a range of practical aspects of community engagement with communities, particularly with immigrant/racialized communities, by running a community-engaged program of research on the health and wellness issues of immigrant/racialized communities in Calgary, Canada. In this article, we focus on a crucial early step of community engagement-understanding the community ecosystem. The community ecosystem refers to its human, social, and cultural makeups. Understanding this ecosystem requires conscious efforts to comprehend the demography, participate in socio-cultural events, identify community spots, reach out to hard-to-access groups, find the community champions and communication channels/organizations, and reaching out to them to establish relationships. Understanding the community ecosystem allows us to identify the pivotal factors, key actors, and pulse of the community that we are engaging with. This enables us to build mutual trust and goals for research and knowledge mobilization. Subsequently, an empowered, continual, and collaborative partnership becomes possible, resulting in sustained and desirable outcomes.
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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 | 0.134 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.006 | 0.040 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".