Conceptualizing citizen participation in open data use at the city level
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
Purpose Open data initiatives represent a critical pillar of smart cities’ strategies but remain insufficiently and poorly understood. This paper aims to advance a conceptualization of citizen participation and investigates its effect on open data use at the municipal level. Design/methodology/approach Based on 14 semi-structured interviews with citizens involved in open data projects within the city of Montréal (Canada), the paper develops a research model linking the multidimensional construct of citizen participation with initial use of open data in municipalities. Findings The study shows that citizen participation is a key contributor to the use of open data through four distinct categories of participation, namely, hands-on activities, greater responsibility, better communication and improved relations between citizens and the open data portal development team. While electronic government research often views open data implementation as a top-down project, the current study demonstrates that citizens are central to the success of open data initiatives and shows how their role can be effectively leveraged across various dimensions of participation. Originality/value This paper proposes a conceptualization of citizen participation on open data use at the municipal level. Citizen participation is a found to be a key contributor to the use of open data through four distinct categories of participation, namely, hands-on activities, greater responsibility, better communication and improved relations between citizens and the open data portal development team. This paper demonstrates the critical role of citizen participation in open government.
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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: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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 it