Keep Exploring, Sharing, and Tweeting: Connecting Millennials, Social Media and Destination Canada’s Brand
Bibliographic record
Abstract
Social media has become a powerful influencer in its ability to sway customer intentions and behaviors in an online setting. Given the importance of social media and its users in acting as information spreaders and disseminators, particularly in the context of global tourism, the goal of this research is to profile and/or understand youth travellers within the context of their social media behaviour. Using latent class analysis which helps to identify unobserved subgroups within a population, this study utilizes the rich dataset offered by Destination Canada which gives valuable traveler-focused information across the globe, including Brazil, China, Australia, Germany, South Korea, United Kingdom, and more. The results of this quantitative analysis reveal important differences based on age, explorer types and lifestyles, and geographic location as it relates to Canadian travel behaviors. By understanding what motivates these millennial-aged travelers particularly, destinations can create an environment where their actions are better anticipated and encouraged. The contribution of this original research is an empirically-informed view of how travelers share their experiences via social media.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".