Evolving Nexus Between Diplomacy and Comparative and International Education
Bibliographic record
Abstract
Queries are raised about the linkages of universities to diplomacy and international affairs vis-à-vis the major functions of universities – teaching, scholarship and research, and public engagement. Are they quite distinct? Should they be related? This chapter explores such questions by: (1) articulating various concepts and dimensions of diplomacy and soft power; (2) examining select American and British university rankings and league tables; (3) portraying domestic and geopolitical matters via the explication of how various American and British government agencies and philanthropic foundations are affected by diplomacy and foreign policy via their missions and funding priorities for education; and (4) fusing diplomacy and international educational studies. In this exploration, it is posited how comparative and international education and “parent disciplines” are discussed in terms of the aforementioned four items. In essence, there are evolving nexuses among phenomena that influence the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".