Empowering communities through data literacy: a qualitative study exploring Indigenous Australian perceptions, engagement and understanding of data
Bibliographic record
Abstract
Data literacy is an essential competency needed to use data to inform decisions and participate in contemporary society. Importantly, in the context of Indigenous health it enables engagement with health services and facilitates health management, Indigenous data sovereignty and Indigenous data governance. However, while Indigenous data sovereignty and Indigenous data governance are gaining momentum globally, there are no mechanisms for understanding or enhancing data literacy within Indigenous communities. To explore this, a research project was co-designed between the QUT Centre for Data Science and the Aboriginal and Torres Strait Islander Community Health Service Brisbane (ATSICHS), a community-controlled health service in Queensland Australia, to provide insights into the current state of data literacy, community perceptions of data, and identify community suggestions for enhancing data literacy. Furthermore, by utilizing an Indigenist research design, the project ensured Aboriginal and Torres Strait Islander Peoples’ ways of knowing, being, and doing were privileged and prominent throughout research design, data collection and analysis. The qualitative study included 20 semi-structured interviews with Brisbane Aboriginal and/or Torres Strait Islander Peoples who had accessed or engaged with ATSICHS. This paper presents insights into Aboriginal and Torres Strait Islander Peoples’ perspectives on data and data literacy, which may benefit community-controlled organizations and other Indigenous communities within Australia and around the world.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".