Grantsmanship and the University: Five Strategies for Grant Professionals Working with Faculty
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.
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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 | 0.038 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.052 | 0.039 |
| Scholarly communication | 0.033 | 0.021 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.019 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".