A Canadian company supporting Brazil’s 1964 coup: the case of Brazilian Traction Light and Power
Bibliographic record
Abstract
The paper analyzes the role of one of the largest Canadian corporations, Toronto-based Brazilian Traction Light and Power, in supporting the 1964 business-military coup in Brazil. It argues that Brazilian Traction partnered with other corporations and military personnel through the Institute for Social Research and Studies (IPES) to destabilize then-president João Goulart’s administration (1961–1964), paving the way for a military dictatorship amenable to foreign capital. As its guiding framework, the paper draws on the methodology developed by Argentine trailblazer scholars on corporate accountability related to their 1976–1983 dictatorship. It also uses Meta Stephen’s typologies of business-dictatorship collaboration and René Dreifuss’ analysis of the organic elite’s role in the 1964 coup. The study advances work undertaken by Brazil’s National Truth Commission, analyzing the support companies provided to the military dictatorship. The sources analyzed include existing accounts of the nature of the coup and the military regime, Brazilian Traction archives and various reports, editorials and analyses provided by newspapers and magazines.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".