Changing Repertoires of State-Society Interaction under Lula
Bibliographic record
Abstract
During Luis Inácio Lula da Silva’s two administrations as president of Brazil (2003–10), an entirely different kind of actor took over government decision- making. These are the conclusions of an innovative study by Maria Celina D’Araújo, who examined the social and political profile of upper- echelon federal government personnel during that period. She found that for the first time in Brazilian history, former union activists participated substantially in high levels of government, a fact that may not be that surprising considering that the Workers’ Party, and especially the President, had strong links to that sector. Under Lula, D’Araújo found that ‘about 26 percent of Ministers in the first term and 16 percent in the second came from labor unions’ (2009, p. 117). The ministers were also closely connected to more broadly defined social movements: 43 percent in the first administration and 45 percent of those in the second participated in some way in movements, compared to around a quarter of ministers under the previous two presidencies (Ibid., p. 120). For D’Araújo, these numbers suggest that the Lula government represented a more diverse array of interests than seen in the past. Our argument in this chapter is that, in that context, social movements and state actors creatively experimented with historical patterns of state- society interaction and reinterpreted routines of communication and negotiation.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.035 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".