A Picture Is Worth A Thousand Shares: The Case of Destination Canada’s Social Media Visual Storytelling Campaign
Bibliographic record
Abstract
This case is designed to assess the visual storytelling social media strategy created by Destination Canada (DC), the country’s national destination marketing organization, regarding their objective, to drive demand among international travelers, to uncover insights into how it has performed since the devastating impact of COVID-19, and how the future might unfold. DC curates and shares a variety of unique photos and videos to their Instagram page on a near-daily basis, inspiring users to connect emotionally with the brand, develop a desire to visit, and ultimately choose to travel to Canada. Despite success to date with their strategy, DC has faced unprecedented challenges in the face of the COVID-19 pandemic, which have made their path forward unclear. In this case, students will draw upon visual storytelling and social media engagement concepts in tourism and marketing to examine key data related to DC’s campaign and Instagram engagement across their posts, and ultimately piece together the key information required to evaluate the effectiveness of DC’s campaign, along with where changes may be needed for DC to remain engaged with users post-pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".