Strategies, Performances and Profiling of a Sample of U.S. Universities in 2012
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it