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Record W4376136174 · doi:10.1177/11771801231169337

Nhaltjan dhu ḻarrum ga dharaŋan dhuḏi-dhäwuw ŋunhi limurr dhu gumurrbunanhamirr ga waŋanhamirr, Yolŋu ga Balanda: how we come together to explore and understand the deeper story of intercultural communication in a Yolŋu (First Nations Australian) community

2023· article· en· W4376136174 on OpenAlexaboutno aff
Emily Armstrong, Yuŋgirrŋa Bukulatjpi, Dorothy Gapany, Lyn Fasoli, Sarah Ireland, Rachel Dikul Baker, Sally Hewat, Anne Lowell

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2023
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
FundersCharles Darwin UniversitySpeech Pathology AustraliaAustralian Government
KeywordsEthnographyReflexivityMetaphorSociologyIntercultural communicationIndigenousKey (lock)AnthropologyLinguisticsEcologyPhilosophy

Abstract

fetched live from OpenAlex

This study explored intercultural communication from the perspectives of partners from different cultural and linguistic backgrounds. We used a culturally responsive form of video-reflexive ethnography to study intercultural communication processes between Yolŋu, pronounced Yolngu (First Nations people from the region that is now called North-East Arnhem Land, Northern Territory, Australia) and Balanda (non-Indigenous people). Yolŋu and Balanda researchers worked collaboratively throughout the study (2017–2021). In a very remote Yolŋu community in northern Australia, five early childhood assessment interactions were recorded and analysed by the 40 Yolŋu and Balanda participants. Researchers analysed data collaboratively using an approach aligned with constructivist grounded theory. We connected key research findings about intercultural communication processes to a place-based metaphor which foregrounds Yolŋu cultural knowledge and encourages reflection on deeper ways of thinking about how we connect, collaborate and communicate interculturally.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.122
GPT teacher head0.376
Teacher spread0.255 · 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.

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

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAlterNative An International Journal of Indigenous PeoplesSame topicHearing Impairment and CommunicationFrench-language works237,207