What’s in a name? A comparison of ‘open government’ definitions across seven Open Government Partnership members
Bibliographic record
Abstract
No longer restricted to access to information laws and accountability measures, “open government” is now associated with a broad range of goals and functions, including public participation, open data, the improvement of public services and government efficiency. The 59 country strong Open Government Partnership (OGP) suggests that consensus on the value of open government is emerging amongst public officials. Similarly, academics have shown a renewed interest in open government as they discuss, debate and critique the meaning and role of open government reforms today. Yet, despite the diverse aims and tools characterizing contemporary open government, public officials and academics typically approach the subject as a cohesive unit of analysis, making sweeping—and generally non-empirical—claims about its implications, without accounting for the homegrown flavours that may characterize open government in practice. Simply put, the practice and study of contemporary open government suffers a lack of definitional clarity: what exactly is open government today, and how does it vary across governments? In response to these questions, this paper analyses the content of open government policy documents in seven OGP member states (Azerbaijan, Brazil, Canada, Netherlands, Kenya, United Kingdom, and the United States), providing the first systematic, empirically-grounded multi-country comparison of contemporary open government. The paper suggests where the term departs from and retains its original meaning, and how its definition varies across different governments
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".