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Record W6998913206

“‘Because they proper black, they go “unna, true!”’ Snapshots of a sociolinguistic ethnography at a First Nations boarding school”

2024· article· en· W6998913206 on OpenAlexaboutno aff

Bibliographic record

VenueUWA Profiles and Research Repository (University of Western Australia) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyConversationVariety (cybernetics)Identity (music)FieldnotesConstruct (python library)Boarding school
DOInot available

Abstract

fetched live from OpenAlex

The participation of First Nations people in boarding schools is often associated with a brutal history of assimilation throughout colonised lands (O’Bryan, 2021). Yet, in Australia, First Nations enrolment in boarding schools continues to thrive, with over 2,200 yearly enrolments (Independent Schools Australia, 2021). While previous research on students’ experiences in boarding has noted that identity and language use are greatly impacted by boarding school experiences (Mander, 2012; O’Bryan, 2016), the sociolinguistic practices of boarders, and how these are deployed in the creation of boarders’ social identities, remain to be explored. What language varieties are spoken in boarding schools among First Nations people? What are these varieties used for? What happens when teens with diverse linguistic repertoires come to live outta country under one roof (Fraiese, Rodíguez Louro & Collard, 2022)? What linguistic features are meaningful, and how do boarders employ them to construct social identities within the school? In this presentation, I begin to explore these questions by introducing a bespoke ethnographic corpus of spontaneous conversation among First Nations boarders collected over 14 months at a Western Australian boarding school. The field site, renamed by the students as St Mary’s Hills to protect the institution’s anonymity, is in Whadjuk Nyungar country, and is the home away from home for boarders from across Western Australia and the Northern Territory. While some students speak traditional First Nations languages such as Walmajari and Miriwoong, and new languages such as Kriol, most boarders are L1 speakers of Australian Aboriginal English, a post-invasion contact-based variety of English used by approximately 80% of First Nations people in Australia (Rodríguez Louro & Collard, 2021: 2). The dataset captures the speech of 34 female and 6 male speakers aged 12-18 years old in conversation with friends and kin. Inspired by Eckert’s (1989) canonical work with adolescents in a Detroit high school, this work provides the first sociolinguistic exploration of the linguistic experiences of First Nations communities in boarding. References Eckert, Penelope (1989). Jocks and Burnouts: Social categories and identity in the high school. New York: Teachers College Press. Fraiese, Lucía, Rodíguez Louro, Celeste & Collard, Glenys (2022). Outta country: Indigenous youth identities in an Australian boarding school. NWAV50. Stanford University. Independent Schools Australia (2021). Independent Boarding Schools Data Review. Mander, David J. (2012). The transition experience to boarding school for male Aboriginal secondary school students from regional and remote communities across Western Australia. Edith Cowan University. O’Bryan, Marnie (2016). Shaping futures, shaping lives: An investigation into the lived experience of Aboriginal and Torres Strait Islander students in Australian boarding schools. University of Melbourne. O’Bryan, Marnie (2021). Boarding and Australia's First Peoples: Understanding How Residential Schooling Shapes Lives. SG: Springer Nature B.V. Rodríguez Louro, Celeste & Collard, Glenys (2021). Australian Aboriginal English: Linguistic and sociolinguistic perspectives. Language and Linguistics Compass 15(5): n/a.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.010
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.173
GPT teacher head0.448
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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