25 Years of Transparency Research: Evidence and Future Directions
Bibliographic record
Abstract
Abstract This article synthesizes the cross‐disciplinary literature on government transparency. It systematically reviews research addressing the topic of government transparency published between 1990 and 2015. The review uses 187 studies to address three questions: (1) What forms of transparency has the literature identified? (2) What outcomes does the literature attribute to transparency? and (3) How successful is transparency in achieving those goals? In addressing these questions, the authors review six interrelated types of transparency and nine governance‐ and citizen‐related outcomes of transparency. Based on the findings of the analysis, the authors outline an agenda for future research on government transparency and its effects that calls for more systematically investigating the ways in which contextual conditions shape transparency outcomes, replicating studies with varying methodologies, investigating transparency in neglected countries, and paying greater attention to understudied claims of transparency such as improved decision making and management .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".