The Future of Film Tourism
Bibliographic record
Abstract
Film tourism refers to a post-modern experience at an attraction or destination which has been portrayed in ‘some form of media representation, such as the cinema screen, television or video’ (Kork, 2018: 5). Film-induced tourism occurs when a tourist visits ‘a destination or attraction as a result of the destination being featured on television, video, DVD or the cinema screen’ (Hudson and Ritchie, 2006: 256). Screen tourism, movie induced tourism and TV induced tourism are other terms commonly used in association with this type of special interest tourism (Riley et al., 1998; Connell, 2005; Connell and Meyer, 2009). Beeton (2005), categorises film-induced tourism based on where the tourism activities occur, namely ‘on-film’ and ‘off-film’ induced tourism. ‘On-film’ induced tourism refers to tourism resulting from where a part of a film is shot and shown on the screen, while ‘off-film’ induced tourism refers to events or artificial destinations actualised through the involvement in films (Beeton, 2005). Film-induced tourism falls under the umbrella of cultural tourism as it represents the cultural heritage of a destination and may be considered an expression of visual arts and local traditions (Gjorgievski and Trpkova, 2012), with Kim et al. (2007: 1351) suggesting it has ‘great potential to advance cultural exchange and understanding’. As an illustration of the size and extent of movies which are filmed away from traditional studios, the 11 Star War movies filmed between 1977 and 2019 utilised a number of location sites throughout the world, in addition to sound film studios in the United Kingdom (Elstree and Pinewood Studios) and Australia (Fox Studio). Locations included the countries of Tunisia, United Arab Emirates, Norway, England, Guatemala, Switzerland, Australia, Italy, Thailand, Spain, Ireland, Bolivia and the Maldives (Obias, 2018a). Only two USA locations were used for filming, both in California, however outside of the traditional Los Angeles and Hollywood sites. Similarly, many scenes in the six Mission Impossible movies spanning from 1996 to 2018 were filmed on location in various parts of the world, including Prague, Norway, Paris, London, Washington, Virginia, Sydney, Utah, Berlin, China, Vatican City, Morocco, Vienna, Kuala Lumpur, Budapest, Moscow Mumbai, Canada and Dubai (Looch, 2018; Obias 2018b). Table 8.1 shows a variety of on-film and off-film induced tourism.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".