Transparency-by-design as a foundation for open government
Bibliographic record
Abstract
Purpose Many governments are working toward a vision of government-wide transformation that strives to achieve an open, transparent and accountable government while providing responsive services. The purpose of this paper is to clarify the concept of transparency-by-design to advance open government. Design/methodology/approach The opening of data, the deployment of tools and instruments to engage the public, collaboration among public organizations and between governments and the public are important drivers for open government. The authors review transparency-by-design concepts. Findings To successfully achieve open government, fundamental changes in practice and new research on governments as open systems are needed. In particular, the creation of “transparency-by-design” is a key aspect in which transparency is a key system development requirement, and the systems ensure that data are disclosed to the public for creating transparency. Research limitations/implications Although transparency-by-design is an intuitive concept, more research is needed in what constitutes information and communication technology-mediated transparency and how it can be realized. Practical implications Governments should embrace transparency-by-design to open more data sets and come closer to achieving open government. Originality/value Transparency-by-design is a new concept that has not given any attention yet in the literature.
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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 | 0.133 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.084 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".