Involving im/migrant community members for knowledge co-creation: the greater the desired involvement, the greater the need for capacity building
Bibliographic record
Abstract
Researchers need to observe complex problems from various angles and contexts to create workable, effective and sustainable solutions. For complex societal problems, including health and socioeconomic disparities, cross-sectoral collaborative research is crucial. It allows for meaningful interaction between various actors around a particular real-world problem through a process of mutual learning. This collaboration builds a sustainable, trust-based partnership among the stakeholders and allows for a thorough understanding of the problem through a solution-oriented lens. While the created knowledge benefits the community, the community is generally less involved in the research process. Often, community members are engaged to collect data or for consultancy and knowledge dissemination; however, they are not involved in the actual research process, for example, developing a research question and using research tools such as conducting focus groups, analysis and interpretation. To be involved on these levels, there is a need for building community capacity for research. However, due to a lack of funds, resources and interest in building capacity on the part of both researchers and the community, deeper and meaningful involvement of community members in research becomes less viable. In this article, we reflect on how we have designed our programme of research-from involving community members at different levels of the research process to building capacity with them. We describe the activities community members participated in based on their needs and capacity. Capacity-building strategies for each level of involvement with the community members are also outlined.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".