Framing Policy Visions of Big Data in Emerging States
Bibliographic record
Abstract
Background Emerging states, such as Brazil, India, and China (the BICs), have big plans for big data and digitalization. Research has identified distinct policy visions regarding how technological advances can facilitate economic development and improve governance. Analysis This article examines how BIC governments frame data-driven ambitions across the diverse issue areas in which governments plan to use big data, as well as how they frame the role(s) of the government and citizens in the era of big data. Conclusion and implications We find clear differences in discussions of big data across the BICs and across issue areas. Moreover, we show the societal changes that governments seek to effect using big data vary greatly in scope, with Brazil and India seeking more fundamental changes than China.
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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 | 0.034 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".