Bibliographic record
Abstract
Abstract As earlier chapters have highlighted, there has been an upsurge of interest in rich countries in the incomes of those at the top of the income distribution. Evidence for some countries, notably the US and the UK, has fuelled a general perception that those at the top have done particularly well in the last quarter century or so, with the remuneration of top executives a source of particular comment. From an analytic point of view, a key contribution has been the use of data from income tax records to investigate these trends over the long term, notably Piketty (2001), Piketty and Saez (2003), and Atkinson (2005) for France, the US, and the UK respectively. This has encouraged others to exploit the potential of data from this source, and in that spirit this chapter uses this type of information to look for the first time at long-run trends in top income shares in Ireland from the 1920s up to the end of the twentieth century.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 teacher head, 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".