Geospatial open data: reshaping citizens and governments, roles and interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it