Canada and Brazil: Shifting Contexts for Knowledge Production” (“Canadá e Brasil: Contextos de mundança para a produção de conhecimento)
Bibliographic record
Abstract
This paper addresses the shifting contexts for knowledge production as they affect researchers in the humanities and social sciences working within Canada and Brazil on dimensions of Canadian studies in the twenty-first century. It argues for closer attention to the meanings that words carry within localities and when they travel, and to the contexts in which they make sense. Using a series of brief case studies, the paper suggests that interdisciplinary attention to democracy and governance questions may require a shift in focus and a widening of responsibility beyond traditional academic and institutional actors, as well as deeper attention to the role of English in politics and higher education, and a shift in focus from the nation-state alone to the sub-regional and supra-regional levels. The rise of a global higher education regime further highlights the need for researchers, teachers, and students to question not only the methodological natio- nalism of nation-based studies, but also the methodological cosmopolitanism that works at the global level alone, locating both of these within the frames afforded by those decolonial and postcolonial studies that value place-based knowledges and the transnational literacies they can generate. In short, globalization is cre- ating conditions in which the development of transnational partnerships in the co-creation of knowledge seems both desirable and necessary.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".