South Asia in the 21st Century: “Catching-Up” yet Overheating
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
Economist Stern (2016) asks now why so little is concretely done against global warming. But consider the huge countries in South Asia and their mighty neighbours. South Asia is poised to become the next set of Asian economic miracles. Yet they face a terrible threat from the environment, as global warming picks up speed together with more and more environmental degradation. Can these more than 2 billion people work and find food and water, if temperature rises more than 2-3 degrees? Can peasants work and survive? And how to generate enough electricity for housing, given increasing water shortages? Without massive financial assistance, there will occur widespread reneging on the COP21 objectives (Goal I-III). The system of UNFCCC with yearly big meetings does not offer an organization that is up to the coordination tasks involved in halting climate change—too much transaction costs. South Asia needs the promised Super Fund badly that Stern anticipated 2007.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 it