Bibliographic record
Abstract
Education is recognized as a human right since the adoption of Universal Declaration of Human Rights in 1948 besides health and shelter. Education for All Goals was established where more than 150 governments have adopted world declaration on Education for All policy to support the universal right for education. The ultimate goal of many countries is to guarantee the optimum educational access rates for improving the quality. Similarly, quality is reflected by a range of indicators, including government spending on education, student/teacher ratios, teacher qualifications, test scores, and the length of time students spend in school. Every investment must be measured against how it can serve such aspects to ensure the ultimate quality of Education for All programs. Investing in education reinforces a society’s wealth and growth, where individuals can easily improve their own personal efficacy, productivity, and incomes. A major challenge lies in defining the ideal education indicators and circumstances among countries; especially poorly developed countries that strive to establish a quality evaluation theme. Therefore, there is need of multifaceted standpoint and reasoning framework to realize educational policy evaluations that can truly contribute to the improvement of educational situation in developing countries and around the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".