Brotherhood for Life?: Determining Effective Commitment Mechanisms that Predict Alumni Involvement in Fraternities
Bibliographic record
Abstract
Scholars researching alumni engagement have principally focused on characteristic differences between donors and non-donors, as well as factors that may impact the decision to donate to an institution. However, giving is not the only form of alumni engagement, though hardly any scholarship explores this other side of the coin. In this study, I ask what aspects of respondents' undergraduate fraternity experiences can be linked to staying involved with their fraternity as alumni. Using original quantitative and qualitative data from a web-survey of 129 alumni representing 12 different fraternities from the University of Pennsylvania, I test various commitment mechanisms employed by fraternities to see how effectively they predict monetary, non-monetary, and composite alumni involvement with the respective fraternities. I find that about one-third of respondents self-identify as being involved with their fraternity though only a quarter claim to have contributed financially, leaving a sizable margin that solely participates non-monetarily. I further find that the degree of undergraduate involvement with the fraternity, proportion of fraternity members in respondents' core network, and inculcation of values-based expectations of membership were all positively associated with alumni involvement. Results suggest that fraternities and similar institutions can increase the likelihood that their members stay involved by affording members opportunities that draw upon the positively associated commitment mechanisms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".