Global Economic Prospects, June 2012 : Managing Growth in a Volatile World
Bibliographic record
Abstract
The year began on a positive note. A marked improvement in market sentiment, combined with monetary policy easing in developing countries, was reflected in a rebound in economic activity in both developing and advanced countries. Industrial production, trade and capital goods sales all returned to positive territory, following the slow growth of the fourth quarter of 2011. Although debt levels in developing countries are lower, several countries (notably Jordan, India, and Pakistan) must reduce their structural fiscal balances to reduce debt to 40 percent of Gross domestic Product (GDP) by 2020 (or prevent debt-to-GDP ratios from rising further). As a result, sharp swings in investor sentiment and financial conditions will continue to complicate the conduct of macroeconomic policy in developing countries. In these conditions, policy in developing countries needs to be less reactive to short-term changes in external conditions, and more responsive to medium-term domestic considerations. A return to more neutral macroeconomic policies would also help developing countries reduce their vulnerabilities to external shocks, by rebuilding fiscal space, reducing short-term debt exposures and recreating the kinds of buffers that allowed them to react so resiliently to the 2008/09 crisis.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.017 |
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".