PARTICIPATORY URBAN SENSING: CITIZENS' ACCEPTANCE OF A MOBILE REPORTING SERVICE
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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