Bibliographic record
Abstract
There is no consensus among economists about the nature of the relationship between economic growth and the goal of improving human welfare. After World War II, economists tended to accept that the benefits of growth would trickle down to the poor. Nevertheless, there is an evidence of rising income inequality and a stubborn prevalence of poverty in many developing countries. Such evidence stimulated active concern with promoting more equitable growth. During the 1980s, poverty-oriented policies were pushed to the background by the need to cope with macroeconomic shocks. Since then, concern with poverty- oriented policy has revived, but not as an alternative to concern about growth. The evidence of the past half century shows that growth is necessary for alleviating widespread poverty. In this regard, income per capita remains the most useful single indicator of development, although it is far from ideal. One limitation is that international comparisons require the conversion of national statistics into common currency units, usually U.S. dollars. A more accurate method uses purchasing power parity (PPP) calculations. This method shows that income differences between rich and poor countries are less acute than one finds in statistics based on exchange rates. Other than income, an alternative comparison method is to use physical measures of structural change such as energy consumption, rural population, life expectancy, and female adult literacy. This paper uses a comparative approach to evaluate the dynamic of the human development index (HDI) in Morocco. This is an important index that measures national socioeconomic development, based on combining measures of education, health, and adjusted real income per capita
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".