Information Kiosk Based Indian E-Governance Service Delivery: Value Chain Based Measurement Modelling
Bibliographic record
Abstract
Globally, e-governance systems are evolving towards wider acceptance. Almost all the countries have embraced e-governance as part of their long term policy. Contemporary e-governance implementation efforts however, are not free from challenges. While some argue in favour of convergence among business, government, civil society etc., many feel citizen acceptance needs to be the primary objective. In cases like European Union (EU), citizen acceptance of e-governance services has become very important because of member-driven benefits. In developing countries, this challenge is enormous despite having prolific growth in e-governance infrastructure. In Indian context, e-governance infrastructure has evolved to a stage of consolidation. National e-Governance Plan (NeGP), National Knowledge Network (NKN) and Unique Identification Authority of India (UIDAI) etc., provide the scope for such consolidation. In this paper, it is argued that value chain management approach is necessary to consolidate the efforts made so far. Consolidation needs wider citizen acceptance and value added services are the basic imperatives. This consolidation phase needs to ensure e-governance efforts having longer life cycles, better convergence and connected e-governance. A measurement and acceptance model is presented in this paper with two case studies drawn from India for validation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".