PARTICIPATORY URBAN SENSING: CITIZENS' ACCEPTANCE OF A MOBILE REPORTING SERVICE
Bibliographic record
Abstract
Urban sensing describes the use of today’s mobile devices to collectively gather information about environmental issues of public interest. Such information and communication technology (ICT) tools can enhance current e-government practices by enabling citizens to actively participate in urban decision making and service delivery. Yet, it is widely unclear whether there is a link between the citizens’ propensity to participate and the use of urban sensing technology. In this study we draw on technology acceptance literature to propose a model for the acceptance of a mobile reporting service, i.e. a sensing tool for reporting urban infrastructure issues to a municipality. The model explains perceived usefulness of urban sensing by the citizen’s degree of environmental awareness and his/her willingness to participate in public affairs. Furthermore, we conceptualize mobile literacy as an important antecedent of perceived ease of use. Empirical tests using data from 200 potential service adopters support these ideas. The findings also suggest that for mobile e-government offerings, perceived privacy risks are not a significant barrier to adoption. These results provide important implications for theory and practice.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".