Bibliographic record
Abstract
This paper synthesizes and develops research undertaken by participants in The North-South Institute project, "Macroeconomic policy choices for growth and poverty reduction" in low- income developing countries.1 The project analysed the features of poverty and growth in seven poor countries of varying circumstances and proposed macroeconomic and growth policies for poverty reduction for them. The research was guided by the question: "How does poverty inform growth strategy?" Our research provides evidence of the channels through which growth and distribution or poverty processes depend on each other and respond to policy together. We encapsulate the messages of these case studies in the following six propositions, discussed at length in the paper: i) macroeconomic stability reduces poverty; ii) land redistribution enhances growth; iii) income poverty traps constrain growth; iv) urban-rural growth disparities drive income inequality; v) regional poverty traps resist growth, and vi) ley growth policies can aggravate poverty gaps. The propositions suggest growth policies that may be either of two types in terms of impact on growth and distribution. They have the potential to enhance both growth and distribution (win-win) or to enhance growth while aggravating income gaps or vice versa (win-lose).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".