Five decades of neoliberal developmentalism in “Least Developed Countries”: A decolonial critique
Bibliographic record
Abstract
Abstract “Least Developed Countries” (LDCs) were identified by the United Nations (UN) in 1971 to consolidate international support measures to address development challenges related to poverty, health and education. Using neoliberal developmentalism and decolonisation as a theoretical framework, this paper analyses key policy documents produced by the UN from 1971 to 2021 to investigate the support measures taken by the UN and other international organizations for addressing development challenges faced by LDCs. A major finding of the paper is that while some attempts were made for integrating LDCs into global trade and economy, international organisations could not translate their policy rhetoric into reality; therefore, LDCs have fallen behind in several developmental sectors such as economy, education and health. As the COVID‐19 pandemic ravaged the world, the historical problems and challenges faced by LDCs worsened. In the context of post‐2030 discussions on setting the next round of Sustainable Development Goals, the findings are significant for devising more effective social policies for LDCs.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| 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 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".