Green education diplomacy and civic soft power: the role of states and tech corporations in shaping sustainability pedagogies
Bibliographic record
Abstract
In an era of intensifying climate crises and expanding intersections of state authority and corporate influence, this article advances the concept of Green Education Diplomacy. It frames education not only as a domestic or sectoral concern but as a strategic resource mobilised across borders to generate legitimacy, civic agency, and sustainability norms. This diplomacy unfolds under analytically grounded hybridity-the blending of state authority, corporate agency, and civic participation into new governance configurations. Based on a systematic comparative analysis of 120 governmental and corporate documents (2008–2025), the study examines three democracies (Germany, Canada, Japan) and three technology firms (Apple, Google, Microsoft). Findings show that states mobilise soft power through curricula, teacher training, and policy frameworks, while corporations exert civic power via digital infrastructures, global partnerships, and cultural branding. This analysis demonstrates how education operates simultaneously as state-centered soft power and corporate civic power, generating legitimacy through intersecting logics, and situates Green Education Diplomacy within soft power theory, neo-institutionalism, and Education for Sustainable Development (ESD). It highlights education as diplomatic performance, corporations as norm entrepreneurs, and states as competitors for green authority, demonstrating that in our digital Anthropocene, the global order is shaped in classrooms as much as in ministries and boardrooms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".