Citizen and government co‐production of data: Analyzing the challenges to government adoption of VGI
Bibliographic record
Abstract
With the recent rise of open government and open data initiatives, governments are increasingly adopting new approaches of citizen participation to support a more democratic, transparent, and inclusive government system. Among other forms of citizen participation, Volunteered Geographic Information (VGI) is one approach to connecting citizens and government. Accepting VGI as a way to update government data or collect near‐real‐time information from citizens can create a partner‐like relationship where citizens voluntarily contribute time and effort to support government actions. However, as a relatively new approach to citizen participation and data collection, VGI requires evaluation from the perspective of governments. This research investigates the challenges and opportunities that governments have found through the implementation of VGI projects. Results are drawn from interviews conducted with 19 government organizations, revealing organizational and technical challenges that limit the adoption of VGI. Organizational challenges are associated with government mindset, implementation, and project management, while technical challenges involve development of the system and quality of data. Given these challenges, we derive recommendations within the phases of project initiation, implementation, and expansion that can foster VGI adoption in government.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".