Reply to Memmott et al: It is Time for Healthy Living Priorities to be Integrated into Indigenous Housing Policy and Practice
Bibliographic record
Abstract
This is a reply to Memmott et al’s (2022) chapter on ‘Aboriginal social housing in remote Australia: crowded, unrepaired and raising the risk of infectious diseases’. It is not hard to see that the physical environment of the home is likely to have a significant impact on health. Problems such as leaking toilets, uneven stairs, mouldy walls and crowding clearly create health-and-safety risks. This relationship is recognised in World Health Organization (WHO, 2018) housing and health guidelines, with crowding identified as increasing risks to mental and physical health. These problems disproportionately affect low-income renters, who have limited capacity to remedy such issues (Robinson and Adams, 2008). This relationship between housing and mental and physical well-being significantly contributes to intergenerational poverty (McKnight and Cowell, 2014). The central role of housing for individual and community well-being has been known for centuries. After all, it is housing’s impact on health that has historically been the primary justification for slum-clearance programmes, and this remains the case in many countries. Yet, policy has been slow to address this connection, even though health services bear most of the cost of poor-quality housing. This is the case for Indigenous housing, where poverty, housing exclusion, underfunding of the social and affordable housing sector, and government neglect more generally result in high rates of crowding and deteriorated dwellings in countries including the US, Canada, New Zealand and Australia (Habibis et al, 2018; Lea, 2020). Despite this, there is a surprising dearth of research on the processes and mechanisms that make many Indigenous homes sites of illness and injury
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.049 | 0.083 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".