Finanzas y Desarrollo, Septiembre de 2010
Bibliographic record
Abstract
All for One examines inequality and the many ways it matters. In our overview article, the World Bank's Branko Milanovic explains how income inequality is measured and tells us that it's increased in most countries. The good news, he says, is that global inequality--between countries--could be on the downturn. IMF economists Andrew Berg and Jonathan Ostry find that a more equal society has a greater likelihood of sustaining longer-term growth. Other IMF research on inequality finds that financial sector development not only 'enlarges the pie' by supporting economic growth but divides it more evenly; that higher income inequality in developed countries is associated with higher indebtedness--at home and abroad; and that while fiscal consolidation is necessary in the medium term, slamming on the brakes too quickly can harm jobs and cut wages, exacerbating inequality. Also in this issue, we profile Elinor Ostrom, the first woman to receive the Nobel Prize for economics. In a tour of the globe, we look at how the African diaspora can help their home countries from afar, try to draw some early lessons from the euro area's debt crisis, investigate how the United States and its neighbor Canada handled public debt--with different results, and find out about the rise of emerging markets as systemically important trading centers. Back to Basics explains the difference between micro- and macroeconomics, and Data Spotlight tells us about a new worldwide survey of foreign direct investment
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.118 | 0.002 |
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; both teacher heads agree on what is shown here.
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".