How is crowding in Indigenous households managed
Bibliographic record
Abstract
Strategies to manage crowding in Indigenous households can reduce the negative effects for people living in those households. However, to permanently reduce crowding, the supply of appropriate houses in Indigenous communities needs to be increased. KEY POINTS• Crowding in Indigenous households has structural causes, including the shortage of appropriately designed and affordable rental housing, and cultural causes, including visiting and sharing practices. • Housing design that caters for large families and visitors would offer the opportunity of fulfilling cultural obligations to house visitors, alleviating some issues of crowding.• While the Canadian National Occupancy Standard (CNOS) is currently used to measure crowding, it does not distinguish between those situations where crowding causes little stress and those where it does have negative effects for residents.• Case studies revealed that the number of people living in the house was not the most significant trigger of stress but the lack of control over who stays and their behaviour.• Locational differences were identified; with those interviewed in the regional centre case study areas (Mt Isa and Carnarvon) less likely to indicate they considered crowding or stress to be a problem.• The most critical mediating factors for coping in large households are: firm administration of house rules by the householder, rules in organising sleeping space in large households and sharing visitors among other family households.
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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 | 0.002 | 0.007 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".