User Behaviours and Spatial Aspects of Navigating Heritage Tourism Sites with a Digital Interpretive Application
Bibliographic record
Abstract
Tourism in its many forms is one of the largest and continually expanding sectors of global economic development. Tourism spaces often contain stories for visitors to explore. These stories are told using various interpretive approaches and tools to familiarize, educate, and entertain visitors. This is especially evident in heritage tourism, since the variety of sites and the degree of visitor interest associated with this form of tourism is high. Rapid technological innovation and high interest in the use of digital tools for tourism have precipitated this study, which examines how visitors engage with a locative media mobile phone application (GuideTags) in digital interpretive experiences in historic downtown Niagara Falls, Ontario. This research specifically explores the behaviour of visitors using a digital interpretive app at a heritage site who were offered a decision between following a prescribed linear tour route or given the autonomy to choose their own route in the same location. Results suggest that a) emerging digital technologies impact the interpretive experience for users, and b) understanding how visitors choose to engage with these tools provides useful theoretical insights for tourism researchers, and practical insights for tourism operators and businesses when creating digitally mediated tourism experiences.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".