International Dependency and the Economic Development of Less Advanced Countries
Bibliographic record
Abstract
Dependency is a double-edge sword that can either promote or demote the status of the dependent. The case of the dependency relationship between less advanced countries (LACs) and most industrialized countries (MICs) is a pertinent example that explains the attitude developed by each party to situate and maintain itself in its current status to be classified as underdeveloped, developing or developed. This paper investigated the effects of international dependency on the economic development of LACs using the analytical approach of secondary data interpretation and found that although the dependency between LACs and MICs is bi-directional, LACs have surrendered all their potentials for attaining economic development to MICs who are steadily designing different devices such as developmental projects aids, structural adjustment programs, MDGs or good governance programs to maintain them in a state of economic and political vulnerability. In view of this, the paper recommends LACs to wake up from sleep and take their destiny in hand by building up strong institutions that can take care of their needed structural changes and basic-needs requirements before embracing the ideas of international dependence revolution models (IDRMs) and market fundamentalism models (MFMs), for no external forces may sacrifice to hold the bull by the horns for them to milk the cow.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".