Beyond Ambiguity: A Practical Framework for Developing and Implementing Open Government Reforms
Bibliographic record
Abstract
The broad idea of ‘Open Government’ is widely accepted as a facilitator for rebuilding trust and validation in governments around the world. The Open Government Partnership is a significant driver of this movement with over 75 member nations, 15 subnational government participants and many others local governments implementing reforms within their national frameworks. The central tenets of transparency, accountability, participation, and collaboration are well understood within scholarly works and practitioner publications. However, open government is yet to be attributed with a universally acknowledged definition. This leads to questions of adaptability and salience of the concept of open government across diverse contexts. This paper addresses these questions by utilizing a human systems framework called the Dialogue Boxes. To develop an understanding of how open government is currently positioned within scholarly works and practitioner publications, an extensive literature search was conducted. The search utilized major search engines, often-cited references, direct journal searches and colleague provided references. Using existing definitions and descriptions, this paper populates the framework with available information and allow for context specific content to be populated by future users. Ultimately, the aim of the paper is to support the development of open government action plans that maximize the direct positive impact on people’s lives.
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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 | 0.171 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.022 | 0.094 |
| Scholarly communication | 0.046 | 0.062 |
| Open science | 0.010 | 0.040 |
| Research integrity | 0.030 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".