The Koolungar ( <scp> <i>Children</i> </scp> ) Moorditj ( <scp> <i>Strong</i> </scp> ) Healthy Skin Project Part I: Conducting First Nations Research in Pediatric Dermatology
Bibliographic record
Abstract
Integrating First Nations knowledge systems and Western research methodologies recognizes the strength, experience, and insight of First Nations peoples in addressing health issues in their communities. In research, this includes projects being led by First Nations Elders and peoples, including First Nations researchers in the team, and collecting data in ways that reflect First Nations ways of knowing, being, and doing. In this paper, we reflect upon the Koolungar (children) Moorditj (strong) Healthy Skin Project; operational in Perth and Bunbury, Western Australia, Australia, where the traditional custodians are the Noongar Aboriginal people. This Aboriginal Elder co-designed project is presented as a case study to illustrate the practical use of The Kids Research Institute Australia Standards for the Conduct of Aboriginal Health Research, in striving towards best practice in Aboriginal pediatric dermatology research. It leads into The Koolungar (children) Moorditj (strong) Healthy Skin Project Part II manuscript, in which we present cross-sectional studies of Aboriginal children attending community skin screening weeks.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".