The Great Separation: Top Earner Segregation at Work in Advanced Capitalist Economies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Earnings segregation at work is an understudied topic in social science, despite the workplace being an everyday nexus for social mixing, cohesion, contact, claims making, and resource exchange. It is all the more urgent to study as workplaces, in the last decades, have undergone profound reorganizations that could affect the magnitude and evolution of earnings segregation. Analyzing linked employer-employee panel administrative databases, the authors estimate the evolving isolation of higher earners from other employees in 12 countries: Canada, Czechia, Denmark, France, Germany, Hungary, Japan, the Netherlands, Norway, Spain, South Korea, and Sweden. They find in almost all countries a growing workplace isolation of top earners and dramatically declining exposure of top earners to bottom earners. The authors perform a first exploration of the main factors accounting for this trend: deindustrialization, workplace downsizing, restructuring (including layoffs, outsourcing, offshoring, and subcontracting), and digitalization contribute substantially to the increase in top earner segregation. These findings open up a future research agenda on the causes and consequences of top earner segregation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it