Open Data and Evidence-based Socio-economic Policy Research in India: An overview
Bibliographic record
Abstract
Public entities are one of the main producers of socio-economic data around the world. The Open Government Data (OGD) movement encourages these entities to make their data publicly available in order to improve transparency and accountability, which may lead to good governance. Thus, OGD can promote evidence-based public policy by supporting empirical research through making quality data available. Hence, in this paper we discuss the current status of OGD initiative in India, how its principles are considered and applied by the public authorities, and the feedback of the research community about OGD in India. Les institutions publiques sont parmi les principaux producteurs de données socio-économiques. Le mouvement « Données Gouvernementales ouvertes » les encourage et assiste parfois dans la mise à disposition de leurs données au public, pour améliorer la transparence, ce qui peut conduire à une meilleure gouvernance. Ainsi, les données ouvertes gouvernementales peuvent conduire à de meilleures politiques publiques basées sur leurs résultats en soutenant la recherche par la publication de données de qualité. Ce document traite de la situation des données ouvertes en Inde, leur publication et usage par les institutions publiques et par la communauté de recherche. Las instituciones públicas son los principales productores de datos socio-económicos. El movimiento de " datos gubernamentales abiertos" alienta estas entidades de poner sus datos a disposición del público para mejorar la transparencia, y la gobernanza. Por lo tanto los datos gubernamentales abiertos pueden promover políticas públicas basadas en evidencia, mediante el apoyo a la investigación empírica a través de hacer datos de calidad disponibles. En este trabajo se discute lo que es la realidad de los datos gubernamentales abiertos en la India, cómo sus principios están consideradas y aplicadas por las autoridades públicas y la comunidad de investigación.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.041 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".