Grantsmanship and the University: Five Strategies for Grant Professionals Working with Faculty
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
Recently, articles in law reviews, academic publications, blogs, newspapers, and magazines have focused much attention upon the changing culture of the academy. As higher education experiences a decline in funding and in tenure-track opportunities for faculty members, there are questions about the value of some core aspects of higher education, such as the arts and humanities. Yet, in light of the current economic downturn, more students of all ages see the wisdom of returning to school for degrees in a wide variety of disciplines. Grantseeking plays an important role in filling the funding gap at colleges and universities and also in supporting innovative and non-traditional programs for new and continuing students. This article discusses some of these trends and then provides valuable advice for grant professionals who are (or want to be) in higher education. In particular, the article uses examples to explore five strategies for grant professionals working with college and university professors. All of these strategies convey the professionalism and value that grantwriters bring to universities.
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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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