Small Towns and Regional Municipalities Implement SMART Solutions, Identified Issues, and Challenges
Bibliographic record
Abstract
In the last decade, SMART services and solutions projects have been concentrated mainly in large and economically strong cities where large populations are concentrated. This is place where the potential is found that predicts return on investment as well as further development. As not all cities are predestined for this type of project, we were interested in how small towns and cities perceive their potential to engage in the implementation of such projects. We believe that the topic of SMART solutions should not be a significant priority only for large cities. We decided to analyze the needs of small cities in terms of implementing SMART solutions. We also tried to identify the challenges as well as the requirements to accelerate this process. In our analysis, we focused on the Slovak Republic, which is a good candidate for such research due to its structure of cities and municipalities. In the process of data collection, we approached more than 2,744 s mall towns and municipalities with a population of up to 5,000 with a request for information, and 547 town and municipality representatives responded to the questionnaire. The results of the research show an interesting and clear finding, s mall towns and rural areas also want SMART. In the research, we identified several not simple problems that need to be solved for the successful implementation of these goals
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".