Articulating AI futures for Brazil: on different regimes of technological solutionism
Bibliographic record
Abstract
The study of expectations in innovation policy has gained prominence over the past decade . A key concern has been the belief that complex social problems can ultimately be resolved through technological innovation, a perspective termed technological solutionism. However, the existing literature mainly focuses on North America and Europe, offering a homogeneous understanding of technological solutionism and a normative view of how these expectations affect the Global South . This paper employs critical discourse analysis, in dialogue with the sociology of expectations, and Latin American science and technology studies, to examine technological solutionism in connection with two recent Brazilian policy documents: the Brazilian Strategy of Artificial Intelligence (EBIA) and the Fapesp Call for Applied AI Research Centers. It argues that, in Brazil, technological solutionism is linked to a very specific concern: the one of dependency. Thus, adopting technosolutionist imperatives would be seen as the best remedy against ‘underdevelopment’, impeling Brazil to ‘leapfrog’ and catch-up advanced nations. The paper calls for critical approaches to regimes of technological solutionism whose consolidation and hegemony are tied to the role Global South countries have in global capitalism and international policy.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".