Can Social Dialogue be Transformational in a Socially Polarised Brazil? Labour Relations under the Third Lula Administration
Bibliographic record
Abstract
Faced with a heterogeneous governing coalition and an unstable geopolitical scenario, the new Lula government is confronting many challenges to successfully enacting the social, economic and political reforms it promised during the 2022 presidential campaign. On the labour front, the current government has already made strides in rolling back some of the most regressive policies implemented during the Temer and Bolsonaro administrations that negated the possibility of real minimum wage increases and hobbled labour inspectors combatting modern-day slave labour. However, due to conflicting class interests, it will be more difficult to revoke key elements of the regressive 2017 labour law reform, which introduced new forms of precarious contracting, restricted access to the labour justice system, and curtailed union financing. This article will present a balance to date of the tripartite efforts to build and enact a pro-worker labour relations reform. We argue that the employers’ group’s path-dependent expectations to maintain many aspects of the previous labour law reform, together with labour’s diffuse support in the legislative branch, makes a more thorough-going reform that is advantageous for workers less likely to be implemented in the current conjuncture.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".