The Smart Cities Approach: The Opportunity and Possibility of Data Driven Communities
Bibliographic record
Abstract
The purpose of this research is to identify the implications of data collection and use of data in the smart cities approach to provide recommendations to mitigate the challenges or reduce the risks associated with these practices. \n \nThe primary research question is: What are the implications of data collection and the use of data in smart cities and how does this affect citizens, businesses, and civil society as a whole? \n \nThe secondary research questions are: \n1.) What government tools and approaches can Canada learn from other countries when it comes to data collection and the use of data in smart cities? \n2.) How do privacy policies affect smart cities data collection and the use of data? \n3.) What are the benefits and problems of data collection and the use of data that the government needs to be aware of when implementing the smart city approach? \n \nThere are many strengths, weaknesses, opportunities, and threats to the implication of data collection and use of data in the smart cities approach. The strengths of collecting and using data in the smart cities approach are to increase efficiency, provide better services for residents, and increase innovation. The weaknesses and challenges are data biases, privacy issues, slow regulation/law development, and limited resource to implement data-driven communities. The opportunities are the implementation of “smart governance,” increase efficiency in government services for the public and create better public policies by using the data collected. The significant threats municipalities need to address are cyber-attacks, data breaching, data ownership, and data sovereignty.
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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".