“She is not white, she is Latina”: Positionality as an asset in cross-cultural ethnographic research with First Nations youth in Australia.
Bibliographic record
Abstract
This paper examines positionality in sociolinguistic ethnography. Using a reflexive narrative approach, I frame my Global South identity as an ‘asset’ [Author] in working with First Nations girls at a boarding school in Australia.<br/>Unlike traditional variationist studies, where ‘the impact of the researcher is typically never discussed or considered’ (Lawson, 2009: 114), in ethnographic variationist approaches, reflecting on one’s positionality becomes central. In their school ethnographies, most researchers have either worked as ‘insider’ researchers within their own sociocultural communities, and/or tried to minimise their presence by not disclosing much about themselves (Eckert, 1989; Moore, 2023).<br/>Here, I follow (Rodríguez Louro & Collard, 2021) in adopting a decolonial approach, interrogating if traditional approaches are culturally appropriate. I reflect on how standing out and welcoming student reflections on my ethnicity helped create bonds with participants during fieldwork. I discuss the tensions that emerged from my positionality as a non-First Nations migrant female researcher and highlight the ways in which shared experiences – such as being away from home like the boarders – created safe spaces for reciprocal researcher-participant relationships. Students’ perceptions of my ethnicity, shown in (1), positioned me as non-white, facilitating candid conversations on race, identity, and discrimination.<br/><br/>Someone said, ‘We’re all Aboriginal here’ and then Courtney said, ‘Not Miss, she’s Latina’. I think my ethnicity / background is exciting to them, and they do not see me as a white person. (…). I like how we can talk about ethnicity freely. It’s not a taboo for us as it often is for mainstream white people as I was reading in ‘White Fragility’ by Robin DiAngelo. <br/><br/>I argue that reflections on one’s positionality are key in contexts of power asymmetries and histories of misrepresentation. This approach enabled me to move beyond traditional frameworks, to foreground participants’ agency in shaping their sociolinguistic worlds.<br/><br/>References<br/>Eckert, Penelope (1989). Jocks and Burnouts: Social categories and identity in the high school. New York: Teachers College Press.<br/>Lawson, Robert (2009). Constructions of social identity among adolescent males in Glasgow. Unpublished PhD Thesis, University of Glasgow.<br/>Moore, Emma (2023). Socio-syntax: Exploring the social life of grammar. Cambridge: Cambridge University Press.<br/>Rodríguez Louro, Celeste & Glenys Dale Collard (2021). Working together: Sociolinguistic research in urban Aboriginal Australia. Journal of Sociolinguistics 25(5): 785–807.<br/><br/>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".