CONCEPTUAL UNDERSTANDING OF EDUCATION AND SUSTAINABLE DEVELOPMENT IN INDIAN CONTEXT
Bibliographic record
Abstract
After industrialization in 18th century, European countries had been developed at a vast pace. The development of new economy had emerged new markets and demand for raw materials. Europeans found new land for settlement like North America, South America and Australia. They found a strong basis for economic development like raw materials, land, transport and labour. These areas began to industrialize and got develop. Now U.S is the world’s most developed nation. North America. Germany, Britain, Russia, Canada, Japan, China are the other developed nations of the world. After the second world war, when Asian and African nations got independence from the colonial rule, the concept of development was truly evolved in comparison of growth. Development is a very vast terminology. It can be seen as processes in social terms, in economic and educational, environmental and political terms. Development is no longer considered to be the same as growth, as it was earlier perceived by developed world. illiteracy, and ill health, and, is one of ensuring participation in government.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".