The ongoing impact of colonisation on childhood obesity prevention: a First Nations’ perspective
Bibliographic record
Abstract
As researchers (HS, HB, AC) whose work focuses on ensuring that the voices of those most affected by the research are central to co-designed solutions, we are in the privileged position of capturing the stories of lived experience behind the data. In this perspective, a story of lived experience illustrates the need to reframe the childhood obesity prevention narrative towards a more equitable approach that ensures strategies reach the most socially disadvantaged populations. Here we come together to share Louisa Whettam's story, which captures common themes depicting drivers of disparities in obesity prevalence among First Peoples of Australia. We thank Louisa for sharing her personal story in this perspective piece as part of highlighting First Nations’ perspective in relation to the ongoing impact of colonisation on childhood obesity prevention. \n \n
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".