The Double Whammy of Pandemic and War: A Systematic Review of India’s Education Diplomacy to Address Educational Inequities in Afghanistan
Bibliographic record
Abstract
Higher education is considered an important tool for the overall development of any country, and it holds true in the context of Afghanistan as well. At the same time, a good eco-environment in terms of political will, leaders’ farsighted vision, a fair budget, good infrastructure, and a good teaching community are some of the basic requirements for higher education to move in the direction of new and higher horizons. However, due to the ongoing war during the last couple of decades, the country’s education system has become out of reach for a substantial part of the population due to poverty, lack of infrastructure, refugees and internally displaced, digital division, etc., critically affecting the education equity. This systematic review examines India’s education diplomacy in addressing the inequities in Afghanistan’s education system and making them more equitable. Education was further dilapidated with the outbreak of the COVID-19 pandemic. Afghanistan is caught between a war and a pandemic and suffers from a double whammy in losses. Subsequently, given their chilling effects, higher education becomes devoid of multiples equities, including education. However, because of their historical and geo-civilizational ties, India has focused on development diplomacy in general and education diplomacy (E.D) in particular to improve educational infrastructures.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".