Evaluating the role of online data availability: The case of economic and institutional transparency in sixteen Latin American nations
Bibliographic record
Abstract
We adopt the principal-agent framework and the asymmetry of information between the principal and the agent in order to approach two subjects of much attention and expectations: i) the formal online release of governmental data as a means of furnishing information, and ii) its contribution to government economic and institutional transparency. We identify important characteristics of transparency as instruments to lessen the information asymmetry in relevant areas (or subjects) where corruption and inefficiency are generally present in political institutions. We focus on the central governments of sixteen Latin American nations. We determine that, while there exists a moderate release of data relevant to areas where corruption generally takes place, its contribution to providing meaningful information to the citizenry is minimal. Our findings also show the importance of policy that explicitly mandates that data corresponding to specific areas where corruption and inefficiency take place be shared over the Internet; adequate levels of national online technical sophistication are not sufficient. We conclude that modern information technologies, as tools to contributing to government transparency and lessen knowledge divides in the evaluated areas, are not meeting expectations. Our framework and findings seek to utilize political science theories to contribute to an early understanding of the role of modern data-oriented technologies in government transparency, and highlight the positive and negative effects that these can have in the betterment of governance and the consolidation of democracy.
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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 | 0.032 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".