Mapping motivations for a Canadian leisure experience : impact of social media engagement
Bibliographic record
Abstract
Mapping Motivations for a Canadian Leisure Experience seeks to reveal the impact \nsocial media has on the leisure tourist experience in Canada. Desired experiences vary from \ngeneration to generation. How influential are informally generated images shared through \nmodern day technology such as social media forums to the leisure experience of a tourist? Does \nthe impact override or align with the formal marketing message intended by destination \nmarketing professionals? Are tourist expectations inflated as a result of communications via \nsocial media? Are experiences deflated due to hyper-communication? Are people too involved \nwith their devices to enjoy or be present during the lived moments that make up their tourist \nexperiences? This report offers an analysis of the impact of social media engagement during the \nleisure experience on the experience itself through a qualitative discourse of perceptions, posted \nsocial media images and text as shared by Australian Millennials as they travel and experience \nthe Canadian leisure landscape. This analysis generates informative data on the relationship \nbetween Millennials, their leisure journey, and social media delivering a social media \nengagement theory that is applicable to future research that seeks to gain further insight into the \neffects of social media on leisure and travel.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".