Bibliographic record
Abstract
The report examines urban tourism by the following indicators – economic impact, management and planning, marketing activities, products and services, human resources, responsibility for cultural and natural heritage, innovations, experiences of visitors in the world tourist destinations. Specific perspectives of the development are outlined based on the analysis of those indicators and some examples of good practice are given. City tourism has a major impact on the economy measured by the employment and economic growth (Cape Town, Buenos Aires). The management and planning of the destination are important aspects of urban development (Buenos Aires, Melbourne). Modern marketing strategies create a brand destination and maintain a good image of the city (Quebec, New York, Barcelona, Lisbon, Quito). The supply of tourism products and services complying with the customer requirements is an effective approach to the successful development of the city on the tourism market (Kazan, Quebec, Cape Town). The staff skills can be a competitive advantage for the destination (Paris). The local community realizes the economic importance of the cultural and historical monuments and landscapes for the visited city (Athens, Kazan, Quebec, Vienna). Creating a competitive environment for innovation and development of new business models at the destination is a factor for the development of urban tourism (Vienna, São Paulo, Quebec, New York, Quito). The study and analysis of the experiences of visitors are necessary in order to offer tourism products which attract visitors and meet their expectations to the greatest extent.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".