Contrastive Analysis on Recommendations for Tourists Visiting Culturally Diverse Metropoles: Brussels vs. Toronto
Bibliographic record
Abstract
It is rare that one would ever find themselves mentioning Toronto and Brussels in the same sentence other than perhaps when talking about one topic: demographic makeup. But when showcasing their home to the world, how do locals use multiculturalism (or not) to talk about or promote a cultural experience? To further investigate any potential latent differences and similarities, TripAdvisor forums pertaining to tourism in both Toronto and Brussels were analysed. The data retrieved throughout these forums served as the backbone for a survey to be administered to locals from each city. The instruments aimed to identify trends on whether locals from either city promote multicultural activities and food or rather perhaps elements of national culture. The study revealed that there were no overwhelming differences between the way locals from either Toronto or Brussels sell their city to tourists. Noteworthy was the discovery that Brussels locals who speak three or more languages responded with more positive attitudes towards encouraging locals to explore all neighbourhoods of their city and to try ethnic food. Along those same lines, when asked about the types of cuisine a local would recommend, nearly all Toronto participants indicated that they would suggest that tourists eat ethnic food whereas almost all those from Brussels responded that they would recommend Belgian food.
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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".