‘Global’ Education: Migration, Belonging and the ‘Rule’ of Being a Guest
Bibliographic record
Abstract
Abstract: This article explores the different issues in education in a globalized society, especially significant as the COVID-19 pandemic has deepened existing challenges for ‘Global’ Education. It reflectively navigates the stormy waters of '[in]difference' in an intertwined understanding of global and local, exploring the complexities of an increasingly globalized world by conceptualizing the words migration, segregation, integration, and engagement in the context of ‘Global’ Education. From vaccines accessibility to economic woes, from natural disasters and climate change to the intensification of migrations often due to the uneven way economics has worked to privilege a few, while disregarding the needs of most; the pandemic has highlighted inequalities, the inclusion of differences in the mainstream narratives of power and dominance is reflected in this article through the lens of ‘Global’ Education: Home and Abroad. Is there hope for a globally inclusive education in a world in need of compassion and meaningful relations?
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".