Where in the world are our #ProudlyUNB alumni? How the University of New Brunswick developed a creative caricature to help locate and engage alumni around the world
Bibliographic record
Abstract
Maintaining connections with alumni once they leave campus can be an incredible challenge, particularly in the early years following graduation when contact information is always changing. Through an innovative digital marketing campaign, UNB’s Alumni Office developed a fun caricature named Freddy John who became the face of the alumni office and set out on a worldwide adventure to locate and engage with UNB’s ‘missing’ alumni. He ‘travelled’ the world over an 11-month period connecting with UNB alumni through a series of digital communication pieces, collecting updated contact information from 6,500+ alumni in over 90 countries and providing new opportunities for engagement and solicitation. Using UNB’s campaign as the primary focus, this paper will review the process of planning a contact information campaign, and developing the communications tactics that best fit the institution. It will also look at UNB’s campaign results as well as ideas for ongoing measurement.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".