Geospatial open data: reshaping citizens and governments, roles and interactions
Bibliographic record
Abstract
New forms of civic data flows are being implemented through government open data and crowdsourced data. This massive increase in volume and speed of data flow, and the use of apps to distribute and collect data, has the potential to radically alter the relationship between citizen and government. I examine the role that these flows (such as municipal transit data) play in framing the user as citizen or consumer.I selected five municipal apps, from Canada's major open data cities, that utilise civic open data or collect data from the public, and then conducted interviews of government and developers for each app. Thirteen respondents took part for a total of twelve interviews. Interviews collected government and developer perceptions of citizen engagement as expressed via open data and civic apps. My interviews also allow me to map the flow of open data to identify how it is produced, and how these arrangements reshape government practices. There was a perception skew towards framing civic app users as citizens, but also an emphasis towards neoliberal government agendas, suggesting a consumer orientation. Interviews resulted in a mapping of the selected open data civic app ecosystems and their influential actors, which revealed some of the potential weaknesses of the outsourcing of government information services.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".