Conflict, extremism, resilience and peace in South Asia; can covid-19 provide a bridge for peace and rapprochement?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
South Asia, home to 1.97 billion people (25% of the world’s population), is no stranger to conflict and confrontation. Longstanding border disputes (such as between India and China and the decades-old standoff between India and Pakistan), the forced displacement of Myanmar Muslims to Bangladesh, and the 2021 rise of the Taliban triggering a mass exodus of professionals and educated women from Afghanistan underscore the enormous volatility and unpredictability of the region. Climate change poses a further challenge, with the real risk of interstate “water wars.”1 Indeed, South Asia now faces a range of threats, with real risks of these spilling over into interstate conflict.\nThe links between longstanding conflict, insecurity, and poverty are well recognised.23 Abject poverty, especially when associated with disparities, underlies many of the known conflicts worldwide, unsurprisingly given the drain conflict places on social sector spending. And although lack of social inclusion and ethnic inequalities have been shown to lead to domestic terrorism,4 economic inequalities and grievances are stronger drivers of rebellion,5 and are particularly relevant in South Asia. Despite robust economic growth and progress on many technological fronts, South Asia still has the world’s largest concentrations of poverty, illiteracy, malnutrition, and preventable maternal and child deaths outside sub-Saharan Africa.6 Widespread poverty is closely intertwined with social disparities, marginalisation on the basis of an egregious caste system, and vast inequities that perpetuate disillusionment, grassroot rebellion, and further conflict.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it