Unveiling the Maze of Researcher's Identity: Navigating Insider or Partial Insider Roles in the Community Engaged Research
Bibliographic record
Abstract
Introduction: Community-engaged research (CER) entails collaborative work with individuals who possess lived experiences and are directly impacted by the issues under investigation. Undertaking research within communities introduces a distinctive dimension, where researchers' own identities assume a pivotal role in influencing interactions, perceptions, and research outcomes. In this context, distinct research identities for researchers have been acknowledged in existing literature. The conventional dichotomy of "insider" and "outsider" research identities has been established. Depending on the degree of alignment or differentiation in terms of identity, culture, experience, or affiliation with the community, a researcher can assume the role of an insider or an outsider. Approach: In this article, we recount our journey in establishing a community-engaged research program within the Bangladeshi-Canadian immigrant community in Calgary, Canada. The research team shares an affiliation with the Bangladeshi-Canadian community, consequently endowing us with an insider research identity. This article encapsulates our experience in this endeavor. Observation: Initially, we presumed that our community background would grant us a significant advantage in engaging and collaborating with community members, given our shared ethnicity, culture, and identity. Yet, as we embarked on research activities like participant recruitment, interviews, and workshops, it became evident that the community perceived us more as partial insiders from a research standpoint. Partial insider refers to individuals having some affiliation with the group under study, though insufficient to qualify as complete insiders. While the community acknowledged our insider status, they also maintained skepticism about our identity as university-affiliated researchers. Consequently, we needed to establish research relationships at the community level, transitioning from community members to researchers through genuine engagement efforts. Conclusion: We learned that being insiders of a community does not automatically guarantee immediate and active engagement for the research, as the community may have different definitions, perceptions, or expectations of insiders. We also learned that being partial insiders requires us to be aware and respectful of the diversity and complexity of communities, and to adopt a flexible and responsive approach to community engagement in research.
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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 | 0.127 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.037 | 0.059 |
| Scholarly communication | 0.029 | 0.025 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".