The Political Economy of Foreign Aid: An Overview of the Carrot and Stick
Bibliographic record
Abstract
In political economy discourse, aid is a focus of world attention. A school of thought sees it as developed nations’ hands of “friendship” to the less developed ones; a way of promoting growth, development and peace. Another school sees it as a way of promoting the national interest of donor nations. This paper examines the political economy of aid from both perspectives to discuss the politics and intrigues involved in the use of carrots and sticks approach. It also dwells on the arm-twisting involved in the allocation of aid by donor nations as well as x-rays the view that foreign aid are more of political economy and less humanitarian. The study found out that less developed countries are the major recipient of foreign aid, and in most cases, aid does not bring the expected anticipated positive changes or development but sometimes leads to crises of arrested development. In conclusion the paper observes that aid by the donor countries is a double edge sword because of the conditionalities often attached: you agree to our terms you get our aid if not you do not. The paper consequently, recommends a more humanitarian aid from donor nations to recipient nations, and also propagates the need for less developed countries to look more inward than outward for development strategy in a globalised world that is veering towards protectionism. The methodology adopted by this paper is analytical, while dependency theory illuminates the study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".