Bibliographic record
Abstract
The Australian results shown in this paper use confidentialized unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) survey. Researchers using HILDA are required to acknowledge that the HILDA Project was initiated and is funded by the Commonwealth Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) and is managed by the Melbourne Institute of Applied Economic and Social Research (MIAESR), and to note that the findings and views reported in this paper are those of the author and should not be attributed to either FaHCSIA or the MIAESR. I am grateful to Bruce Headey and Mark Wooden for advice on HILDA income measures, to Rolande Laterreur Saumier of Statistics Canada for his assistance in using the Canadian data, and to Fred Argy, Hielke Buddelmeyer, John Pencavel, Daniel Ploetzl, and participants at A common critique of most measures of income inequality, which are based on a single year's income, is that they fail to take account of income mobility. If income fluctuations are large, and individuals can smooth consumption, then high inequality and high mobility may be no worse than low inequality and low mobility. To test this, I use panel data from four countries – Australia, Britain, Germany and the United States – and estimate measures of permanent income inequality that are based on income averaged over multiple years. I find that: (1) using pre-government income, annual inequality and permanent inequality have grown in Germany and the US, while post-government income inequality has grown in the US; (2) comparing levels of annual post-government income inequality across countries, the ranking was the
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.332 | 0.126 |
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".