Inequality and technical change: old and new theories of segmentation°
Bibliographic record
Abstract
Since the late 1970s, income inequality has been on the rise in a number of OECD countries, although nowhere it has increased as much as in the UK and the US (Gottschalk and Smeeding 1997). Many factors enter into the determination of income inequality. Low pay and unemployment, especially of people other than head of family, play an important role in determining households ’ poverty, while government redistributive programs exert an important compensatory role- the latter for instance account for the stability of income in Canada and most European continental countries (Ruiz-Huerta et al. 1999). Income inequality is strongly correlated with the evolution of earnings inequality. While the US (and the UK) have experienced the sharpest increase in wage inequality, recent research seems to suggest an acceleration in the widening of wage differentials and an increase of the “working poor ” also in some continental European countries (cf. Ruiz-Huerta et al. 1999 for comparative analysis of wage dispersion; Howell and Huebler 2001 for a critical assessment; Brandolini et al. 2000 for Italy). As in the US and in the UK, the rise in earnings inequality in the EU countries is to be ascribed to changes at the margin of the labour market: a rise in low pay jobs which is accounted for by the increasing importance of new flexible “non standard ” patterns of employment1. A EU comparative analysis on quality in work and social exclusion (EU 2001, p.76) concludes that in 1996 almost a quarter of the European workforce were in jobs of low quality, with the highest share among temporary contract workers, and especially temporary workers in part-time jobs2. The same
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".