From Dual Towards Bipolar? Some Recent Trends in the Indian Economy and Their Implications for Development Theory and Policy
Bibliographic record
Abstract
Though economic growth sometimes gets prime importance in the development policy of some less developed and developing countries, it has been observed that inequality adversely affects economic growth from both the supply and the demand sides in such economies. On the supply side, inequalities of income and wealth (particularly the latter) create imperfections in the credit market. If an economy is demand constrained, an increase in inequality will make matters even worse as the richer section of the people has a lower propensity to consume than the poor. Though the Gini coefficient of income distribution has been declining in regions like Sub-Saharan Africa and South-East Asia during 1981–2023, it has increased in India and North America. This dualistic nature of development has further been overshadowed with polarisation of income and wealth in India with the characteristics of considerable intra-group homogeneity and inter-group heterogeneity. Sluggish growth in real wages, educated unemployment, and the possible adverse impacts of the presently emerging artificial intelligence (AI) can lead our economy towards bipolarity, and our economy is in danger of being divided into two parts with no interconnection working through labour reallocation from the backward to the advanced sector. Needless to say, that would be the end of the idea of development because a developing economy, by definition, means a dual economy with a shrinking backward sector.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".