Revitalization of First Nations languages: a Queensland perspective
Bibliographic record
Abstract
Abstract First Nations languages can play a significant role in ensuring connection to land, culture, Songlines, kinship, history, and stories. Ongoing language attrition for First Nations peoples of Australia has been due to colonization and past adverse government policies, which rendered First Nations languages a threat to the expansion of the colony. Through processes of dislocation from country and punishment for speaking language, many First Nations peoples began to lose their languages and were forcefully compelled to speak the English language on missions and reserves. Promoting First Nations languages in early educational contexts can instill a sense of cultural identity and connectedness to schooling for First Nations children, helping to ensure that languages are passed on to future generations. In many parts of Australia, First Nations languages are being revitalized and are being taught to both First Nations children and non-Indigenous children in early learning centers and in classrooms. This paper draws upon existing literature, briefly examining the removal of First Nations languages in Queensland from a historical perspective. The authors consider three essential elements required to work with First Nations communities when revitalizing First Nations languages and implementing a successful language program into schools: co-design, authentic delivery, and cultural inclusivity. We demonstrate how these elements have been used in the revitalization of First Nations languages in two Queensland schools. Finally, the importance of using an Indigenous centered approach to maintain languages at a local level is posited as a critical step in creating culturally inclusive environments for First Nations children in mainstream school settings.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".