"West Indianness" as an Ethnographic Presentation of Self in the Field: Black Canadians Across The Border
Bibliographic record
Abstract
Researchers have shown ethnography to be a communicating tool of a social world under study, thereby educating the reading audience. However, being in the "field" as a researcher who is marked by a race, ethnicity, and nationality presents challenges to the way our participants see the researcher and therefore to the type of data collected about a given social group. Despite this, researchers tend to push this information into the background of their analyses. This paper considers how a Canadian researcher of West Indian background used "West Indianness" in sociological field research as a methodological tool for participant recruitment and the maintenance of insider status while clearly marked as "other" because of national birthplace. This research stems from an ethnography in gentrified Brooklyn, New York from 2004‐2007 with West Indian childcare providers. Results show how the insider/outsider presentaEon of self as a Canadian West Indian accommodated and at times hindered the research process while in the field. This paper explores how ethnographers can incorporate a more nuanced reflexivity of this insider and outsider status and relate it back to the analysis of their work as they re(present) their 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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.051 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".