Bibliographic record
Abstract
After years of mutual neglect and sometimes outright tension, Canada’s relations with Brazil are smoother than ever. As Brazil emerges as a relatively powerful and influential global player, shouldn’t Canada try to build that increasingly fluid relationship into a strategic partnership, making Brasilia a key prong of its global policy? Looking at Brazil’s place in the world, and at the ways in which its foreign policy meshes – or not – with Canada’s, this paper argues that such an option has little appeal and in fact few chances of success. Brazil’s rise has its limits, and the country’s global reach and power remain heavily constrained. Beyond the Americas, and even within, Brazil’s power is very soft and could hardly be harnessed effectively by Canada. Moreover, the two country’s international agendas do not overlap much. In global governance circles, Brazil’s global star is on the rise just as Canada’s is in decline. On democracy and human rights, Canada is much more willing to put sovereignty between brackets, and in the face of nuclear proliferation, Brazil is much more critical of the asymmetry of the global regime and the advantage this gives to established powers. Crucially, Canada largely embraces globalization and sees Asia’s rise primarily as an opportunity, whereas Brazilians feel more threatened by it. In that context keeping things running smoothly, without dreams of “grandeur, ” is the most that should be sought: normal is great, special would be too much.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.312 | 0.077 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".