Being brave with the brand: Vancouver Island University’s #ILearnHere Instagram campaign and the power of content marketing through social visual communications
Bibliographic record
Abstract
In 2013 VIU’s strategic marketing team launched a successful Instagram campaign that focused on connecting with their target audience in a popular digital location. This campaign intended to boost stagnant enrolment at the University while increasing brand awareness within their region. This paper shares insights on what made this campaign successful with an audience interaction rate almost seven times higher than projected, how they controlled the campaign on a digital platform, and how they created and maintained a hashtag that resonated with their audience and continues to be dominated by the University today, with over 8,000 posts made by users since 2013. The information shared by Debra Jacklin, Strategic Marketing Manager at the time of the campaign, will explore the strategy and tactics developed for this campaign, how VIU measured meaningful results, as well as how the campaign concept was scaled and adapted for other purposes. The tactics shared by John Gardiner will explore how to effectively run the ‘day-to-day’ operations of a social media campaign.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".