Recognizing Development beyond Economic Growth: By analyzing impact of Inequality on Development
Bibliographic record
Abstract
The idea of development has transformed in recent decades from measuring per capita income to improving quality of life of the people. The traditional advocacy for Western models of development has proven to be futile when many developing countries (especially East Asian Tigers) have shown that one size does not fit all and there are other ways to achieve growth as well. However, global economic development also accompanied with another incidence called inequality through distributional bias and limited opportunities in last few decades. When national income is not distributed proportionately to non-economic sectors, the economic success fails to benefit sustained development in a country. This paper focuses on impact of such inequality on human development, poverty and growth. Countries with high ranks in Gross National Income (GNI) often turn into lower rankings in Human Development Index (HDI) when distribution is below the standard in health and education due to lack of pro-poor policy measures. On the other hand, poverty reduction slows down and incident of extreme poverty also rises in regions where inequality is high. Besides, rising inequality with declining share of bottom 40% of population results into inability of economic system to create jobs and such weak growth governance leads to lowering of growth rate in the long run. This paper is articulated based on secondary materials from different sources in order to study impact of inequality. It provides further opportunity to work in this subject with much broader and primary data in future.
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How this classification was reachedexpand
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".