Brazilian Federal Institutes and Canadian Colleges: Sharing Experiences Internationally
Bibliographic record
Abstract
The Brazilian Federal Network of VET Institutes was created in 2008 to address the demand for Higher Education’s rapid growth. Since the establishment of Science Without Borders in 2011, the Federal Institutes have been developing international strategies for strengthening their internationalization process. However, there has been little research about the theme in Brazil. This article highlights the cooperation between Canada and Brazil that enhanced the Federal Institutes’ internationalization process. The findings presented in this article are part of the research results on the Brazilian Federal Institutes’ internationalization model, which used Situational Analysis as a methodological tool and pointed to the A Thousand Women project as the first significant international experience in these institutions. The data analyzed supports the claim that Canada became a significant reference for the Brazilian Federal Institutes and helped them build their internationalization process concretely and collaboratively.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.054 | 0.013 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".