Outsider Championship of Insider Research: A Collaborative Approach to Community Engaged Research in Cross-Cultural Settings.
Bibliographic record
Abstract
Introduction: The researcher's positionality is among the numerous factors influencing community engaged research (CER) outcomes in cross-cultural settings. Factors include community traits, research topic, methods, participation, collaboration level, and communication quality, all intricately intertwined. This intricate interplay precludes a universal approach, emphasizing the necessity of customizing each CER venture based on distinct context and research objectives. Approach: Our research program addresses challenges faced by immigrant/racialized communities in Canada, marked by considerable racial/ethnic diversity and cultural distinctions. While working with immigrant communities different from my own, I may be seen as an outsider researcher by those communities due to our differing identities. Outsider researchers lack shared identity with their research communities, facing access and rapport challenges. On the other hand, we also had insider researchers from those communities in our team. In this article, we recount our journey in establishing a CER program within several immigrant/racialized communities in Calgary, Canada. Observation: As outsider yet trained researchers, we possessed expertise and resources that could benefit the community we engaged with, including technical skills, academic credentials, funding prospects, and network connections. Our outsider status also allowed us to bring a fresh perspective and critical viewpoint to the research topic, unburdened by community assumptions or norms. Nevertheless, outsider researchers encounter challenges like mistrust, resistance, misunderstanding, or skepticism within the community. Conversely, our insider team members leveraged their insider knowledge and community access to bolster trust-building, rapport, data collection, and interpretation. Insider researchers also acted as community advocates and allies, equipped to understand and address needs. Ultimately, the outsider researchers assumed a championing role, facilitating the insider researchers' efforts. Conclusion: Our cross-cultural CER experience illustrated how we managed the challenges and embraced the opportunities stemming from our team's mixed positionality, blending outsider and insider members. The strategy of championing insider research by outsiders, involving facilitation, support, and empowerment, proved instrumental in advancing our research program. We also recognized that adopting either insider or outsider roles mandates sensitivity to community diversity and complexity, alongside a flexible, responsive approach to research engagement.
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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 | 0.146 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.029 | 0.040 |
| Scholarly communication | 0.029 | 0.014 |
| Open science | 0.007 | 0.040 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".