The Bolsa Família and Brazilian strategic narrative in the age of Lula and Dilma
Bibliographic record
Abstract
This article uses the theory of strategic narrative to study the way Brazil presented its conditional cash-transfer program Bolsa Família abroad. More specifically, it studies where and how that message was received under both Luiz Inácio Lula da Silva and Dilma Rousseff from 2003- 2014. Previous academic work on the Bolsa Familia has not addressed how it fits within the larger Brazilian foreign policy strategy of the 21st century to increase autonomy in its international relations, especially with developing countries in the \\"Global South.\\" As such, this article attempts to address this deficit by using text analysis of twenty-seven countries\\' English- speaking media coverage of the program to hypothesize that Brazil used the program as an extension of its activist foreign policy to create a larger international role for itself. The timeframe for the article begins with Lula\\'s expansion of the program during his first term in 2003, at a time when Brazilian foreign policy shifted towards greater insertion of national autonomy into a Western-dominated international system. Whereas Lula\\'s foreign policy worked to reignite Brazil\\'s long-held ambitions for international relevance, Dilma\\'s administration oversaw the end of Brazil\\'s \\"ascension\\" moment, based on a decline in foreign investment, administrative malfeasance, a declining economy, and an abandonment of previous insertion strategies such as \\"activist\\" foreign outreach and a commitment to exerting political capital abroad. Even though the Bolsa Família remained a constant throughout both Lula and Dilma\\'s administrations, the findings from this article suggest a change in international perception between the two leaders\\' administrations, giving credence to the idea that for Brazil, the figure who drives the narrative is important. From the \\"Global North,\\" the American, Australian, Canadian, and English media generally trended from positive to negative sentiment between Lula and Dilma\\'s term, while developing countries such as Nigeria, Zimbabwe, Pakistan, India, and Ghana reacted gave a warmer reception to it. These findings suggest that Brazil\\'s strategic narrative was best received by partners in the Global South, suggesting a correlation with Lula\\'s ambitious foreign policy approach that expanded a foundation present in the Fernando Henrique Cardoso administration to attract Southern allies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".