Bibliographic record
Abstract
Abstract Objective – The authors analyzed seven years of sponsored research projects at the University of Illinois Library at Urbana–Champaign with the aim of understanding the research trends and themes over that period. The analysis was aimed at identifying areas of future research potential and corresponding support opportunities. Goals included developing institutional research themes that intersect with funding priorities, demystifying grant writing and project management through professional development programs, increasing communication about grant successes; and bringing new faculty and academic staff into these processes. The review and analysis has proven valuable for the Library’s institutional practices, and this assessment may also inform other institutions’ initiatives with grant-writing. Methods – The authors performed a combination of quantitative and qualitative analyses of the University Library’s grant activities that enabled us to accomplish several goals: 1) establish a baseline of data on funded grants; 2) identify motivations for pursuing grants and the obstacles that library professionals face in the process; 3) establish a stronger support structure based on feedback gathered, and through collaborations with other groups that support the research process; and 4) identify strategic research themes that leverage local strengths and address institutional priorities. Conclusions – Analysis of Library data on externally funded grants from the University’s Proposal Data System provided insight into the trends, themes, and outliers. Informal interviews were carried out with investigators to identify areas where the Library could more effectively support those who were pursuing and administering grants in support of research. The assessment revealed the need for the Library to support grant efforts as an integral component of the research process
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.
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 | 0.069 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".