Bibliographic record
Abstract
For most Indians, the North East (NE) has remained largely on the fringes of nationhood as well as on the periphery of the country’s geography. This is partly because India has ignored the region politically and economically for a long time, and partly because the complex social and cultural dynamics have made it difficult to integrate the region with the rest of the country. However, India can never achieve sustained high economic growth or become a powerful integrated nation if it continues to think of developing NE as a rural infrastructure project. It is a region of seven states – Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland and Tripura – that has four international borders – China, Myanmar, Bangladesh and Bhutan – and accounts for a major source of hydrocarbons (oil and gas), coal, limestone, tea, bamboo and other resources. A big, bold, tangible, all-encompassing strategy is suggested in this article to kick-start an economic revolution in the NE, using domestic businesses and partnerships neighbouring South East Asian countries. The paper uses the model of the Delhi Mumbai Industrial Corridor (DMIC), a $90 billion effort funded jointly by Government of India and Government of Japan to make western India into an economic powerhouse. Part 1 of this paper examines the political and economic landscape of the region and explains how diplomacy, policing and development brought peace to Assam and to NE at large. Part 2 proposes a major new economic plan for the future, with Thailand as partner.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".