Bibliographic record
Abstract
• Objectives/Research questionsThis research seeks to better understand giving behavior by alumni to their former university, in a context of the progressive expansion of university foundations. We identify motivations for and impediments to giving and, more specifically, raise the question of attachment to the university and its effects on donation.• Methodology/approachAfter exploring the literature on factors influencing donation to higher education institutions and on attachment theory, we conducted a qualitative study based on interviews with 25 alumni.• FindingsThis research highlights factors of attachment and non-attachment to the university, linked to identity, nostalgia, geographical location, the education received, and a feeling of community. It shows that an alumnus who is attached to his or her university will be willing to support it to a greater extent financially. Finally, we explain that various motivations (reciprocity, support for education and professional integration, tax exemptions) and disincentives (dissatisfaction, limited financial resources, high tax bracket, other priorities) influence giving behavior.• Managerial/societal implicationsEncouraging cohesion through project-based teaching, helping and supporting students in their schooling and professional integration or involving them more in university life are possible ways of increasing attachment and favoring donation.• OriginalityWe carried out a literature review on the factors contributing to giving to universities and, for the first time in marketing research in France, identify attachment factors and their link to donation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.979 | 0.989 |
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; both teacher heads agree on what is shown here.
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".