Towards Achieving Education For All: Realizing Sustainable Development Goals Through Space Systems and Artificial Intelligence
Bibliographic record
Abstract
Education for All project of Nanritam, an Indian non-profit, is an ambitious yet necessary idea born out of the difficulties faced during COVID-19 pandemic. One of its main projects is the Filix School established in 2014 in a remote rural and economically backward area of Purulia, West Bengal, India with the aim of providing holistic, equitable and quality education to the socio-economically challenged children of surrounding area. Filix School has very successfully implemented a unique research-based experiential pedagogy over the past decade, significantly improving the academic outcomes of these children. However, the pandemic meant that the school had to provide education by digital means. Thus, ideated that the education provided to the students of Filix school could be leveraged to a larger community. Co-created by school students under supervision, the Filix Innovation Hub has created an artificial intelligence enabled system that provides education to remote areas, including through space systems. Whereas the project is based in India, it may be customized for other parts of the world. This project bolsters the idea that excellent and contextualized quality education with the help of digital transformation can be instrumental to achieve the United Nations’ Sustainable Development Goals. For this project, Nanritam has partnered with a non-profit space policy initiative - ACES Worldwide, reiterating the importance of interconnectedness and the need of space systems in communications between and with remote areas.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".