Bibliographic record
Abstract
Tourism is a vast phenomenon, with substantial economic, social, and environmental impacts for travellers, residents, businesses, and communities (Mason, 2015). Much attention is given to urban destination marketing (e.g. Papadimitriou, Apostolopoulou, & Kaplanidou, 2015), and tourism’s potential for economic and social development in rural or developing world communities (e.g. Lane & Kastenholz, 2015). However, the existing and potential role of tourism in urban residential neighbourhoods has received limited attention. Business Improvement Areas (BIAs) are “association[s] of business people within a specified district, who join together, with official approval of the City… aimed at stimulating local business… [serving] as an economic and social anchor, helping to stabilize and revitalize the local community” (TABIA, 2018b). There are 82 BIAs across Toronto’s diverse neighbourhoods, and many more across the province and country (OBIAA, 2018). However, despite their number, there is limited research on BIAs in general, and specifically on tourism (Ward, 2006). The purpose of this exploratory study, therefore, is to explore and understand BIAs’ roles and actions relating to tourism such as product development, marketing, management, and socio-cultural and economic implications, drawing from interviews with 30 BIAs in Toronto.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 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".