Leveraging influencers to tell an authentic brand story and drive return on investment: Case study of Travel Alberta and Expedia Media Solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Travel Alberta, the official tourism marketing agency of the Province of Alberta, was looking to increase awareness for the region by captivating the imagination of US and Canadian travellers through unique, compelling content. Together with Expedia Media Solutions, it built a campaign that showcased the diverse experiences that Alberta has to offer, increased general awareness for the region and drove consumers to engage and interact with the brand through social media and aspirational videos. The campaign combined high-impact and tactical brand placements on Expedia points of sale in the USA and Canada, a branded Expedia blog, social media outreach, customised video content and influencer engagement. Top travel bloggers were sent to Alberta, where they captured video content around tourist destinations to serve as blog and video ad content. The campaign was a huge success, resulting in more than 81,000 room nights and 22,250 airline tickets booked to and in Alberta. It also drove more than 4.1 million social media impressions across Facebook, Twitter and Google+.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it