Smart Management Systems in Cities and Their Marketing: Case of the Waterloo City in Canada
Bibliographic record
Abstract
Abstract Competitiveness of cities forces the city and public sector representatives to invent new methods of management and use the innovative thinking. Success of cities, based on Etzkowitz and Leyedesdorff (2000), has to take into account new strategies of co-operation of the academic institutions with the local authorities, entrepreneurs (in our case in tourism business) and new graduates focused on high-tech industries and start-up businesses. This trend is based on the principles of New Economic Geography (Krugman, 1994; Porter, 1998) and the new Theory of Growth (Romer, 1990), which enforce the importance of knowledge capital and smart technologies. Hjalager (2002) supported the idea of the importance of the institutional innovations and Ward (1998) mentioned that universities and research institutes are key entities to promote smart technologies and decisions in a city (Triple Helix concept). The purpose of the chapter is to discuss the results of research conducted in Waterloo, Canada, Ontario, which belongs to the Ontario Technological Triangle. Waterloo is a city of two universities, Waterloo University and Wilfred Laurier University. The purpose of the chapter is to discuss the results of research conducted in Waterloo, Canada, Ontario, which was focused on the competitiveness growth through the implementation of the smart management systems (Triple Helix Model) in the city marketing and governance. Some of these approaches influenced also tourism business due to multiplication effect and the growing competitiveness is a source of a continual growth of students, visitors and entrepreneurs to the city and the region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".