The dualisation of unemployment compensation in emerging economies: Brazil, China and Russia
Bibliographic record
Abstract
Abstract This article examines the development of a ‘dualised’ welfare regime –generous earnings-related unemployment compensation for ‘insiders’ andresidualneeds-based social assistance provision for ‘outsiders’ – in China and Brazil, which experienced impressive economic development in recent decades. It argues that such a welfare outcome can partly be accounted for by the ongoing influence of ‘insiders’, which was conditional upon the pace and nature of economic liberalisation reforms and their representation in institutional channels of social policy-making. It also demonstrates that the new social/unemployment assistance schemes for ‘outsiders’ emerged due to both governments’ fear of losing their power in politics; yet, these schemes were designed in a residual way since ‘outsiders’ did not possess the same political resources as ‘insiders’ did. The paper, moreover, draws from the Russian experience (a negative case) to demonstrate that such a dualised welfare outcome did not take place because the ‘insiders’ were weak, owing to a radical and orthodox liberalisation and they did not have access to institutional venues to influence social policy-making process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".