Confronting the Insider-Outsider Polemic in Conducting Research with Diasporic Communities: Towards a Community-Based Approach
Bibliographic record
Abstract
Researchers focusing on diasporic contexts face the difficult task of wearing their “academic hats” while at the same time building meaningful relationships with immigrant communities. This is no more apparent (and important) than with “non-community” (i.e., outsider) researchers. Here diasporic communities, having already experienced the trauma of forced migration, must see the academic researcher as one they can trust and who is invested in their long-term well being. In this paper I address methodological and philosophical concerns related to the insider-outsider researcher distinction and to conducting research as an “outsider.” The principle aims of the paper are to critically examine the distinctions that create and perpetuate the insider-outsider polemic, explore what this polemic “looks like” within diasporic contexts, and consider community-based participatory research as one “vehicle” that might effectively address some of the thorniest problems associated with the insideroutsider distinction.
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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 | 0.367 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.035 | 0.205 |
| Scholarly communication | 0.035 | 0.031 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".