The perfect synergy: Alumni, donors, students, employers — A case study in Silicon Valley
Bibliographic record
Abstract
This is a case study about overcoming internal institutional silos to develop a new market for alumni engagement, donor cultivation, and student and alumni job opportunities. Simon Fraser University (SFU) was unknown in the Bay Area until three key areas of the university banded together to form the Bay Area Working Group — a cross-functional team to develop a comprehensive strategy for that region. The paper discusses specific strategies that were developed to address the following goals: (1) increasing the number of activities delivered in the region, while maximising strategic outcomes for broader institutional needs, (2) coordinating a single delegation to participate in one or two annual trips to the region, (3) increasing the number of organisations that hired co-op students by using alumni as door openers, (4) integrating current students into alumni-based activities in the region, (5) increasing university pride and loyalty held by alumni in the region, and (6) increasing recognition and acknowledgment of the SFU brand. Five years later, SFU is a leader in the region with record levels of alumni engagement, an increase in the number of financial gifts being realised, significant growth in the number of student co-op positions being posted, and the likes of Facebook, Google, Microsoft and Apple recruiting on an active basis on campus. A list of recommendations are provided to guide others who are keen to both enhance the outcomes realised in existing areas and expand activities into new markets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".