Supporting Informed Destination Development Using Visitor Intelligence
Bibliographic record
Abstract
Abstract Understanding of the profile of visiting markets can assist destinations to make informed and effective marketing investments. This case study describes a collaborative model to design and pilot a community-based visitor experience study on Vancouver Island intended to create a system for ongoing, local data for tourism development. Initiated in 2013 by two communities and Vancouver Island University, the model expanded across the island to 9 communities by 2015 due to its success and community buy in. The model intercepts visitors on their trip asking them to complete a ballot with their email address in exchange for a chance to win a set of attractive prizes from the destination. In exchange, visitors are later sent a web-based survey by email asking about their experience, preferences, satisfaction and characteristics. The project has enabled participating communities to learn more about their visitors and to enhance their marketing intelligence. The project is evaluated with communities annually at a meeting where refinements are made for successive years. This project highlights that systems to provide locally relevant data on visitors are valuable to assist communities to allocate their scarce marketing dollars effectively. The case study describes the elements in the design of the model, the process used to gather data, the tools used to share results and the feedback from the community stakeholders involved. Insights gained are valuable to those interested in modernizing data collection on visitors at the community or regional level. VIU logo WLCE logo Information Vancouver Island University World Leisure Centre of Excellence © N.L. Vaugeois and P. Parker, 2015
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".