From Nativeness to Strangeness and Back: Ascribed Ethnicity, Body Work, and Contextual Insiderness
Bibliographic record
Abstract
This article offers a reflection on a certain variant of broadening the position of “being inside” with some “buts,” or through “within but.” Drawing on my field experience in the Ukrainian diaspora in Canada, I discuss the context-dependent, fluid and labile insiderness and the case of using a researcher’s embodied distinctions (senses, ethnicity, class) in the research site created by the fieldwork participants, and not the researcher him/herself. My considerations are embedded with the dialectics (not opposition) of the insider–outsider and point to the contextual “nativeness” and “strangeness” of the researcher. I also discuss the fluidity and contextuality of a researcher’s field familiarity, as well as when s/he conducts research in cooperation with “their own people,” as well as circumstances and factors that transform this familiarity into strangeness. I argue that the latter, instead of being an obstacle or barrier in the research, is a beneficial and mind-opening ethnographic tool.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".