Monetary and Multidimensional Poverty in Australia: A Dual Measurement Approach
Bibliographic record
Abstract
ABSTRACT Australia's 2024 poverty rate is the highest it has been since 2001. Despite a lack of official poverty measures, recent data has shown that poverty affects 14.4% of the population including one in six children. These rates are higher than when Australia became a signatory of the Sustainable Development Goals (SDG) in 2015, steering it further off course from the goal of halving the proportion of the population living below the national poverty line by 2030. Without an agreed‐upon national definition and measures of poverty, it is also hard to meaningfully track progress. Marking the 50th anniversary of the Henderson Inquiry First Main Report, which first called for a national poverty measure, this paper revisits that call with new urgency. Drawing on Australia's current context and international examples, it proposes a dual approach to poverty measurement – monetary and multidimensional – and presents empirical findings from an illustrative model applying both. The paper examines the relationship between monetary and multidimensional poverty and the insights gained by measuring the two side‐by‐side that neither can yield in isolation. It concludes with recommendations for a legislated national poverty measure, informed by lessons from Canada and New Zealand, which implemented similar frameworks in recent years.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".