Conceptualizing citizen participation in open data use at the city level
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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How this classification was reachedexpand
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 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 | 0.028 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".