Poverty in Australia 2022: A snapshot
Bibliographic record
Abstract
This report provides a brief overview of levels of poverty - overall and among adults and children – following the recent release of Australian Bureau of Statistics (ABS) data on household incomes in 2019-20. It examines trends in poverty since 1999, and through each quarter of 2019-20. While it is unusual to measure changes in poverty on a three-monthly rather than annual basis, it was a very unusual year. In the second quarter of 2020 COVID-19 lockdowns triggered a sharp recession and the government introduced unprecedented public income supports to keep household and business incomes afloat. As we argued in a previous Poverty and Inequality Report released in 2021, these two factors worked in opposite directions: the recession and widespread job losses increased poverty while the COVID income supports reduced it. This report reveals the overall impact of the pandemic recession and temporary increase to income support on poverty (noting that the COVID income supports introduced in the June quarter of 2020 were later withdrawn). As with previous publications in our Poverty and Inequality in Australia series, this report will be followed by a more detailed examination of poverty levels among different groups in the community and their likely causes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".