Educational Poverty by Design: A Case of Mismanagement of National Resources
Bibliographic record
Abstract
The primary purpose of this paper is to review and evaluate the causes of educational poverty in less developed countries. The basic intent in carrying out such a study is to define and derive the role of governing agencies in deliberately creating educational poverty in the country, so that the private interest of the rich and powerful ruling class can be fully safeguarded. This study is of crucial interest to the common man because majority of the people living in less developed countries are poor in spite of the fact, that almost all these countries own ample human and material resources. However, the common man in these countries is continuously suffering, generation after generation, and has been denied access to basic amenities of life. The rich and powerful ruling class, in majority of the less developed countries, has intentionally denied basic education facilities to its people for keeping them ignorant and unaware of their fundamental rights to share national resources and to gain competence for comfortable living in the society. The paper advocates a complete reversal in economic growth policies of the less developed countries so that top priority is given to those projects and programs that directly benefit the common man in the society. In this respect, the author calls for awareness among the people to exercise their economic and social rights so that people of all the strata can share equally the fruits of growth and prosperity.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".