Evaluating the usefulness of VGI for citizen co-producing city services from citizen perspective: A case study of crowdsourcing pedestrian navigation
Bibliographic record
Abstract
With over 50 % of the world's population now living in the cities and the number continuing to grow, cities are increasingly responsible for delivering services to people and businesses. Recent developments in volunteered geographic information (VGI) have provided new opportunities for improving city services by enabling citizens instantly and collectively share and report issues. However, the usefulness of VGI for such use has not been evaluated from a citizen perspective. This paper aims to bridge this research gap through a case study that innovatively uses geosocial media, as an example of VGI, for reporting accessibility issues to local governments and for providing customized navigation services to the general public. Particularly, a study website was developed that allows citizen participants to evaluate the usefulness of geosocial media for issue reporting and for pedestrian navigation. The results suggest that citizens consider geosocial media useful for helping them maneuver dynamic urban environments and for providing a convenient tool for issue reporting. These results suggest that citizens evaluate the usefulness of VGI differently from government officials and that VGI can facilitate government-citizen communication as well as the provision of customized public services, both of which are important to the development of smart cities.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".