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Record W4381248920 · doi:10.1080/00909882.2023.2222163

A flow that comes when we’re talking: water metaphors for exploring intercultural communication during early childhood assessment interactions in a Yolŋu (First Nations Australian) community

2023· article· en· W4381248920 on OpenAlexaboutno aff
Emily Armstrong, Elaine Maypilama, Yuŋgirrŋa Bukulatjpi, Dorothy Gapany, Lyn Fasoli, Sarah Ireland, Rachel Dikul Baker, Sally Hewat, Anne Lowell

Bibliographic record

VenueJournal of Applied Communication Research · 2023
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
FundersCharles Darwin UniversitySpeech Pathology AustraliaAustralian Government
KeywordsEthnographyIntercultural communicationReflexivityIndigenousRelevance (law)MetaphorSociologyPedagogyPolitical scienceSocial scienceAnthropologyEcologyLinguistics

Abstract

fetched live from OpenAlex

Culture mediates how all people think and communicate and intercultural communication skills are required for effective collaboration. This study (2017–2021) explored intercultural communication with 40 participants in one very remote First Nations Australian community in Northern Australia. We explored the perspectives of both Yolŋu (First Nations Australian people from North-East Arnhem Land) and Balanda (non-Indigenous people, in this case Australian) on interactions during early childhood assessments of Yolŋu children (0–6 years). Our intercultural research team used a culturally responsive form of video-reflexive ethnography, a Yolŋu approach to in-depth discussion and collaborative analysis. In this article, we explore nine intercultural communication processes that were recognized and enacted by study participants. Each process is represented by a metaphor drawn from water traveling in North-East Arnhem Land. We share these processes so that others may consider exploring their relevance in other intercultural communication contexts.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.003
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 head0.334
GPT teacher head0.462
Teacher spread0.127 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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