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Record W2743873676 · doi:10.22215/sjcs.v5i1.300

"West Indianness" as an Ethnographic Presentation of Self in the Field: Black Canadians Across The Border

2013· article· en· W2743873676 on OpenAlexaboutno aff
Tamara Mose Brown

Bibliographic record

VenueSouthern Journal of Canadian Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsInsiderEthnographyReflexivityNationalityEthnic groupSociologyGender studiesField researchReading (process)Presentation (obstetrics)Field (mathematics)Participant observationMedia studiesSocial scienceAnthropologyImmigrationPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.140
GPT teacher head0.530
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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