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
Bibliographic record
Abstract
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".