Growth versus Distribution: Does the Pattern of Growth Matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the fashion for pro‐poor growth, there remains no consensus as to its meaning. This article proposes three possible definitions, and examines the pattern of growth over time and in different world regions. The growth of the poor's income can be broken down into a growth effect and a distribution effect. In 143 growth episodes, it is found that the growth effect dominates. However, in over a quarter of cases changes in distribution played a stronger role than overall growth in increasing income for the poor. Econometric analysis of growth regressions for each population quintile supports the idea that openness benefits everyone, but indicates a robust perverse relationship with governance. There is also evidence of a trade‐off between growth and distribution, suggesting that attention to distribution will be better for the poor than going for growth.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it