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
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".