Aspects of Development Financing After the Financial and Economic Crisis
Bibliographic record
Abstract
Published by Palgrave MacmillanThe financial and economic crisis saw developing and emerging countries experience more severe setbacks in their growth rates than industrialised countries and they did not all have sufficient funds to finance robust stimulus measures. The major emerging economies have nevertheless recovered quickly and are currently the most important growth engines in the world economy.Private capital flows collapsed, leaving the global South with an overall deficit in financing. Greater official financing flows have not yet been able to compensate for the shortfalls and the slow increase in private capital flows since the end of 2009 has not been able to do so either. Overall, according to the UN, more capital flows from the South to the North than vice versa. The South thus continues to finance the North.Discussions regarding a reform of the global financial and economic order are ongoing but to date have had little impact on developing countries. The international financing institutions do have more funds at their disposal, but developing countries are still under-represented. The IMF and the World Bank have begun to question some of their previous dogmas. Opinions are divided on whether one can already speak of a new policy.The debate on the role of taxation in the mobilisation of local resources for development financing has intensified. Insight favouring comprehensive reforms of the taxation systems in developing countries has sharpened, but technical aid provided by industrialised countries to realise these reforms is still insufficient. Taxation is acquiring growing recognition as an instrument of State-building, democratisation and governance. The campaign to deal with international tax evasion and illicit capital flows is gaining momentum and the exchange of information on tax issues has improved. However, it is difficult to establish newer and more trenchant instruments for improved transparency, given the predominant interests of shady centres of finance.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".