MOBILE VALUE ADDED SERVICES – THE POTENTIAL DRIVER FOR SOCIAL & ECONOMIC DEVELOPMENT
Bibliographic record
Abstract
The mobile value-added services (MVAS) market is growing rapidly in Rural India. According to a consultation paper published by the TRAI (9) , the VAS contributes around 10%-14% of the total revenue of mobile telecom service providers. A confluence of factors such as the falling costs of value added services, enhanced handsets qualities, lowering age profile of mobile users have helped in stimulating the growth in this segment. The Indian rural sector, at present suffers from decelerating productivity growth rate in terms of economy. It is essential to catalyze agricultural productivity, raise rural incomes, and empower the rural population to make best use of the existing and available infrastructure and funds. The increasing penetration of mobile networks and handsets in India, therefore, presents an opportunity to make useful information more widely available. This could help rural India operate more efficiently and overcome some of the other challenges faced by the social and economic sector. It is therefore timely to take a look at the impact of mobile value added service in rural India.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".