The Great Separation: Top Earner Segregation at Work in Advanced Capitalist Economies
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".