Tourism conversion and place branding: the case of the Olympic Park in Montreal
Bibliographic record
Abstract
Purpose – Many Olympic cities are faced with the challenge of converting various remaining infrastructures after the Games have been held. These infrastructures, often imposing and highly specialized, require local actors to innovate and engage in an urban renewal process that can be very complex and expensive when trying to give them a second life as tourism sites. The paper aims to discuss these issues. Design/methodology/approach – Based on an online survey administered to international travellers ( n =5,553) and 36 semi-structured interviews with local stakeholders, this study shows that the Olympic Park, mainly through the stadium, has international recognition. Findings – However, the sustainability and development of these attractions will need a major overhaul with its welcoming amenities and the integration of the surrounding neighbourhoods in the regeneration plan. Originality/value – Among these facilities, we have the Montreal Olympic Stadium, which is often identified as the architectural jewel of the games and is used as an urban flagship in tourism development strategies. This situation raises several questions not only in the field of tourism, but also the fields of urban studies, leisure and sociology. This paper will examine the case of the Olympic Park in Montreal and its urban regeneration concepts and place branding that have been integrated into the tourism strategies since the early 2000s.
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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 | 0.000 | 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 | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".