Internacionalização da educação: tendências globais e desafios nacionais
Bibliographic record
Abstract
Currently, education specialists have dedicated themselves to debating the existence of national educational policies considering standards and concepts brought about by globalization. This thematic dossier shows that despite the pressures and orientations exerted by neoliberal globalization and International Organizations (IO), there is still room for national educational policies that consider the historical connection of education, the power relations in the academic space, and the specific dynamics of educational actors (teachers, researchers, administrative staff, managers, etc.). Both nationally and internationally, researchers have been focusing, from different theoretical frameworks and methodological approaches, on understanding the phenomenon that we will refer to in this dossier as the internationalization of education. The diversity of approaches, perspectives of analysis, and terminologies adopted reflect the complexity of the theme, as we can observe in the studies developed by Bourdon (2002); Laval and Weber (2002); Dale (2004); Robertson and Dale (2002); Beech (2006, 2009, 2012); Akkari (2011); Lauwerier and Akkari (2011); Costa (2015); Oliveira (2020); Ball (2020); Verger (2019); Shiroma (2020), among others.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".