Bibliographic record
Abstract
There are several explanations for the inequality upswing in the literature: rising globalization, the institutional re-structuring of the nation-states, as well as changes in the relation between the demand for and the supply of skills. Concerning demand changes, Kuznets and Lewis identified two inequality affecting mechanisms with regard to the agriculture-to-industry transition: sector bias — that is, inequality within sectors — and sector dualism — that is, inequality between sectors. In this article it is analyzed whether there are analogue effects on inequality from the sectoral change to the knowledge society. Following the strategy of a most-similar design and a variable oriented approach the hypotheses are tested cross-nationally and longitudinally in 19 OECD countries between 1970 and 1999. To verify sectoral effects, error component models are computed regressing the Gini-coefficient on a globalization measure, the union density, the educational attainment as well as the employment and income differential in the knowledge sector. The results show that some amount of the inequality upswing in the last few decades can be explained by the sectoral change to the knowledge society.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".