Partnering with organisations beyond academia through strategic collaboration for research and mobilisation in immigrant/ethnic-minority communities
Bibliographic record
Abstract
Community-engaged research needs involving community organisations as partners in research. Often, however, considerations regarding developing a meaningful partnership with community organisations are not highlighted. Researchers need to identify the most appropriate organisation with which to engage and their capacity to be involved. Researchers tend to involve organisations based on their connection to potential participants, which relationship often ends after achieving this objective. Further, the partner organisation may not have the capacity to contribute meaningfully to the research process. As such, it is the researchers' responsibility to build capacity within their partner organisations to encourage more sustainable and meaningful community-engaged research. Organisations pertinent to immigrant/ethnic-minority communities fall into three sectors: public, private and non-profit. While public and private sectors play an important role in addressing issues among immigrant/ethnic-minority communities, their contribution as research partners may be limited. Involving the non-profit sector, which tends to be more accessible and utilitarian and includes both grassroots associations (GAs) and immigrant service providing organisations (ISPOs), is more likely to result in mutually beneficial research partnerships and enhanced community engagement. GAs tend to be deeply rooted within, and thus are often truly representative of, the community. As they may not fully understand their importance from a researcher's perspective, nor have time for research, capacity-building activities are required to address these limitations. Additionally, ISPOs may have a different understanding of research and research priorities. Understanding the difference in perspectives and needs of these organisations, building trust and creating capacity building opportunities are important steps for researchers to consider towards building durable partnerships.
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".