September 2010GLOBAL POVERTY AND THE NEW BOTTOM BILLION: WHAT IF THREE-QUARTERS OF THE WORLD’S POOR LIVE IN MIDDLE-INCOME COUNTRIES?
Bibliographic record
Abstract
This paper argues that the global poverty problem has changed because most of the world’s poor no longer live in poor countries meaning low-income countries (LICs). In the past poverty has been viewed as an LIC issue predominantly, nowadays such simplistic assumptions/classifications can be misleading because a number of the large countries that have graduated into the MIC category still have large number of poor people. In 1990, we estimate that 93 per cent of the world’s poor people lived in LICs. In contrast, in 2007 8 we estimate that three-quarters of the world’s approximately 1.3bn poor people now live in middle-income countries (MICs) and only about a quarter of the world’s poor – about 370mn people live in the remaining 39 low-income countries, which are largely in sub-Saharan Africa. This is then a startling change over two decades. It implies there is a new ‘bottom billion ’ who do not live in fragile and conflict-affected states but largely in stable, middle-income countries. Further, such global patterns are evident across monetary, nutritional, and multi-dimensional poverty measures. In reaching this conclusion, the
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".