Regenerative tourism through multicultural placemaking
Bibliographic record
Abstract
Regenerative tourism closely aligns with the principles of place-making, contributing to the revitalization of ethnic heritage landscapes and the communities that inhabit them. This study commentary explores the intersections of immigration and tourism development in Toronto, highlighting the significant role that multicultural diversity plays in rebuilding communities disproportionately affected by the COVID-19 pandemic. The discussion offers a critical analysis of Toronto’s current tourism policy, evaluating the extent to which regenerative principles are integrated into its strategic framework. It emphasizes the transformative potential of multicultural place-making in reshaping tourism through community-driven resources and regenerative urban development. By adopting a place-based lens, the study addresses existing gaps in understanding the importance of immigration histories to multi-ethnic urban environments and their capacity to foster urban regeneration.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".