A Study On Multi Stakeholders' Perception Towards Bengaluru As A Smart City
Bibliographic record
Abstract
The idea of smart cities and their productiveness has taken place in many countries. India, being one among them, has also initiated the scheme to establish smart cities. The purpose of this study is to explore the awareness, opinion and acceptance of the Bengaluru as a smart city, from its stakeholders’ perspective. Using four major factors; urban, administrative, social and technological infrastructure, the level of preparedness and challenges have been identified. This paper has used simple random sampling method. Questionnaires were the mode of data collection using a 5 point Likert scale. A total of 970 samples were taken from various places in Bangalore which includes government, private and public for the study and statistical tools like SPSS and PLS were used to analyse the survey results. The survey has been taken from students, working professionals both private and public, Entrepreneurs and homemakers. The result shows that the 3/4th of the stakeholders from Bangalore has less relationship with awareness, acceptance and opinion also preparedness of smart city which includes urban, technology, administration and social were the impact are low among the stakeholders of Bangalore only quarter part of the stakeholders are prepared, so 3/4th of stakeholders have to be much more cautious in order to adapt on various factors which involves in smart city process in Bangalore and quarter part of the people were little bit knowledge regarding technological infrastructure and significantly less knowledge with regards to administrative, urban and social infrastructure. Helps identify areas where knowledge gap is high; can educate people in those areas.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".