Bibliographic record
Abstract
Abstract Recalibrating tourism in India translates to much more than merely pumping in government resources or investing in overnight technology-led solutions that will put urban landscapes on the global map. The nationally led ‘Smart City’ movement needs to be a participatory process that models a collaborative approach as seen in international cases that have truly taken the concept from paper to practice and beyond. This chapter considers basic definitions of Smart City and Smart Tourism and presents what it takes for the seamless orchestration of smart experiences. This article first traces the evolution of Smart City practice with a sampling of global intelligent destinations that have exhibited successful intersections of urban development with tourism, whilst considering a brief overview of Indian initiatives, efforts and successes. Motivating factors to become smart and sustain the effort are also discussed to highlight hurdles faced and opportunities that await potential Smart Cities, given the growing appetite for such innovation. The chapter concludes with recommendations arising out of this analysis and reiterates how stakeholder inclusion and co-creation play an indispensable role in making this concept a responsible, sustainable and feasible reality for Indian destinations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".