Bibliographic record
Abstract
Smart city, a social system project, is important for urban development as its goal to transform city into a modern city at the real-life technology and solve local innovation challenges. The transformation is a momentous decision, because while benefits are alluring to users; however, hiding beneath are unseen risks. To discover the dangers coming from our smart city, we ask the following question: What if smart city is harmful by design? A problem which is followed by several concerns about personal data collection and privacy violation can exist in this new form of civilization. If personal data privacy is violated, the trade-off may cost lots of money, reputation and even our lives. This paper explores that most of smart city services are relying on cloud to various levels, raising a concern on security and privacy of citizen due to the effect of cloud issues. Therefore, assessing citizen’s capabilities to engage in resilience practice and proactively improving them, is crucial to mitigate damages by data breach. We propose a Citizen Resilience Assessment (CRA) framework that aims to determine the factors that can potentially affect citizen’s data privacy and hinder them in implementing resilience practice. We design a Scale of Citizen Resilience with Vulnerability Risk indicator to evaluate and utilize the vulnerability risk levels associating to the capabilities of citizen’s resilience practice. To illustrate the practicality and usefulness of the CRA framework, we implement it in case study with Canada and discuss our result.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".