Ethical Dimensions of Shared Ethnicity, Language, and Immigration Experience
Bibliographic record
Abstract
In this article I illustrate how some commonalities that I share with my participants―ethnic background, native language, and immigration experience―create unexpected ethical concerns. I explore how these commonalities facilitate the establishment of rapid intimacy, at the same time creating the temptations of overidentification and blurring the boundaries between researcher and participants. Drawing on three episodes from my ethnographic field work, I demonstrate how the mundane and taken-for-granted encounters with informants (used synonymously with participants) reveal the seeds of ethical dilemmas when put under the powerful and critical lens of reflexivity. Instead of viewing ethics as adherence to a set of codes, I explore reflexivity as ethical practice. Researchers continually make on-the-fly decisions in the field and take corresponding actions without the luxury of careful forethought. I argue that such decisions need to be unpacked after the event to examine if they carry ethical implications with them.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.050 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| 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; 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".