Strategies, Performances and Profiling of a Sample of U.S. Universities in 2012
Bibliographic record
Abstract
The global economic crisis is affecting performances of not-for-profits. At the same time donors are targeted by a pressing good-cause related marketing, so that the competition for philanthropy is particularly keen. U.S. universities can be public, not-for-profit and for-profit. U.S. not-for-profit universities are confronted with different marketing, fundraising and revenue diversification. Above all, marketing concerns customers and their segmentation and their purchasing-power exploitation; fundraising aims to gain the trustworthiness of donors, instead. The aim of this paper is the analysis of the revenue diversification of a sample of 100 U.S. not-for-profit universities according to IRS (Internal Revenue Service) Forms. These 100 U.S. universities had the highest 2012’s revenues for the Guidestar ranking (www.guidestar.org). The cluster analysis gives evidence that the highest gain and the highest solvency are both connected with the implementation of revenue diversification for one profile. The most crowded cluster is the Marketing Expert with the second highest gain.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".