Fund-Raising Management of Chinese University Foundations from the Perspective of Alumni Donation Willingness: A Case of Universities in Hennan Provice
Bibliographic record
Abstract
This article reviews the research results of a large number of Chinese and foreign scholars, and combines social identification theory, social exchange theory, social capital theory, stakeholder theory, and principal-agent theory to build a model of individual donation willingness in Chinese universities. The questionnaire was designed, and after collecting and analyzing the data, the hypothesis was verified, and the regression relationship between willingness to donate and different motivations was obtained through response analysis. In addition, this article takes alumni identity as an intermediary variable, and studies the interaction between donation motivation, alumni identity, and willingness to donate. Based on the above research, this article concludes that there is a strong correlation between the motivation and willingness of individuals to donate to universities. Among them, reputation motivation, social responsibility realization, consolidate the relationship, tax incentives, and warm motivation have a significant effect on the willingness to donate. In addition, there is a strong correlation between alumni identity and willingness to donate. It is believed that these research conclusions can enable universities to recognize the purpose of individual donations, and to raise funds more effectively and reasonably for these motivations, thereby expanding the source of school funding and achieving sustainable development of the school.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".