Recent grad refresh: Carleton University’s transformed approach to building relationships with new alumni
Bibliographic record
Abstract
As alumni donor bases continue to mature, devoting resources and energy to welcoming new alumni and cultivating philanthropic relationships is essential. Following consultation with recent graduates, Carleton University developed a refreshed strategy to guide communications, engagement and solicitation with its newest alumni. In the two years since implementation, beginning in 2019, Carleton has seen an increase in the number of new donors and revenue, as well as positive indicators of engagement. This paper outlines the approach Carleton took to audit its existing practices and offers a comprehensive account of the process of developing and implementing this new strategy. The refreshed approach is one that emphasises inclusion and provides value to alumni. This revamped strategy has bolstered donor acquisition, resulting in an increase in the number of recent grad donors and revenue while improving engagement with new alumni. Specific tactics used to cultivate and solicit recent grads are detailed, demonstrating their effectiveness in developing better relationships with alumni at Carleton.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".