Two researchers reflect on navigating multiracial identities in the research situation
Bibliographic record
Abstract
Despite the increasing interest in the experiences of multiracial individuals, as evidenced by the emergent body of literature and research related to multiracial experiences, we lack an understanding of methodological concerns related to research with multiracial individuals. Here, we seek to (1) investigate the applicability of theories of insider/outsider status to research conducted by and with multiracial individuals, (2) interrogate our own research experiences as multiracial scholars conducting research with multiracial students, and (3) identify implications from our analysis for other researchers. We conclude that understandings of methodological terms related to monoracial populations are limited in their applicability to research with multiracial individuals. Additionally, we conclude that navigating multiracial identities in research situations is a particularly complicated process aided less by a shared sense of identity or community between researcher and participants and more by experiences that stem from a similar need to engage in micronegotiations of racial and ethnic identities.
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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 | 0.055 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".