Bibliographic record
Abstract
This survey of 2,201 Toronto residents highlights the impact of local hosting on tourism, culture, and the economy in the Greater Toronto Area (GTA). A notable 80.7% of the GTA population has hosted out-of-town visitors in the past three years, with recent immigrants (91.7% of those in Canada for ten years or less) being significant contributors. The average GTA host welcomes 4.3 groups of visitors, with the most active hosts being recent immigrants aged 25-44 years. Hosts frequently engage in local tourism, joining guests in visiting attractions and attending festivals, a trend particularly evident among newer immigrants. This engagement fosters community pride and increases awareness of local offerings. The report suggests leveraging these activities for destination marketing and cultural engagement, advocating a multi-partner strategy involving residents, neighborhood associations, and local governments. This could include targeted marketing campaigns and sharing hosting stories to enhance the region's appeal. Specific trends among hosts include a higher likelihood of hosting among those with higher incomes and older residents. Immigrant hosts show greater activity in exploring attractions with guests. Over three-quarters of hosts take vacation days to accommodate visitors, underscoring their commitment to hosting. These findings present a significant opportunity for developing tourism and cultural engagement in the GTA, emphasizing the role of residents as ambassadors and participants in the local tourism sector.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".