The CUT’s Experience during the Workers' Party Governments in Brazil (2003-2016)
Bibliographic record
Abstract
This paper looks at the development of the Unified Workers’ Central of Brazil (CUT) during the four consecutive Workers’ Party (PT) governments, first under Luiz Inácio Lula da Silva and later under Dilma Rousseff. The analysis draws on various aspects of the power resources approach, but focuses specifically on institutional power. The government found it politically difficult to implement a left-wing programme, due to the complex nature of the ruling coalition and its conservative opposition in the broader context of neo-liberal hegemony and financial globalisation. By continuing to establish dialogue with social movements, the PT governments stimulated forms of social participation in developing public policies, reinforcing existing institutions and creating new ones. By using its institutional power, the CUT was able to strengthen its participation in public institutions. There were hardly any substantial debates on labour or employment conducted without the CUT’s participation. On the other hand, the privileged spaces in the labour arena did not achieve structural changes capable of redefining the country’s development model and the standard of work regulation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".