n-Logue: The Story of a Rural Service Provider In India
Bibliographic record
Abstract
Can rural Information Communication Technology (ICT) be an effective tool to bridge the digital divide? How can the Internet help developing nations and their disadvantaged in particular? Those who lobby in favour of rural ICT believe that Internet is not just a means of communication but is also an enabler of livelihood in rural areas and therefore, power. For a very long time now, the developing world has carried the burden of colonization and slavery. This has resulted in a lack of confidence among developing economies and the belief that they are not at par with the rest of the world. The lack of "access" has curtailed their ability to compete. In fact, ingenuity and hard work hasn't been adequate for one to enjoy economic and social benefit. In order to acquire these benefits, access to resources like education, health and employment become critical. Internet has been a boon in this regard. Today, one can be in the remotest corner of the world and as long as there is access to Internet enjoy access to education, health and resources. This allows them to compete and use their ingenuity and hard work to bring about a significant difference to their lives. This paper concentrates on how ICT can affect the lives of rural people in the developing world. The total rural population of the developing world is about 3.5 billion, with their average per capita income being no more than $200 per year. India, with 700 million rural people located in 600,000 villages, is a reflection of the developing world. The key issue that is addressed in the paper is whether technology can bring about a difference in the lives of people who earn less than half a dollar a day. Can health and education be made available to them? Can they afford the Internet? And ultimately, can it significantly enhance their livelihoods and income?
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".