The iterative process of staying relevant in measuring alumni engagement
Bibliographic record
Abstract
Accurate measurement of alumni engagement has often been described as somewhat of a mythical creature in postsecondary advancement. The task of creating a measurement tool is daunting, not because it cannot be done but rather because the usefulness of the tool is wholly dependent on what is meaningful to the work of the institution in which it resides. Once a tool is developed, appropriately vetted and implemented, it is imperative that sustainable business infrastructure is in place for it to evolve and adapt with institutional changes over time. This paper provides insight on the process that was undertaken at the University of Calgary to evolve and enhance the Alumni Engagement Scoring Model developed prior to the merger of Development and Alumni Engagement in the organisational structure of the institution. Readers will be provided with strategies to diversify and enhance existing tools to meet the needs of new user groups, sustain buy-in of multiple stakeholders through change and implement systems to stay nimble in a progressive environment. In addition, insight will be provided on the process used to make informed decisions on how to determine metrics that would be most useful for the University of Calgary and the reporting that has been developed as a result.
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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 | 0.215 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".