Supporting tomorrow’s research: Assessing faculty data curation needs at Georgia Tech
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
T oday's researchers face multiple chal- lenges regarding the management and preservation of their data. Consider that researchers are producing and collecting vast amounts of data at an ever-increasing rate. They contend with increased pressure from sponsors, institutions, and the broader public to provide evidence for research outcomes. And funding agency mandates are becoming increasingly demanding, an example being the National Science Foundation's (NSF) requirement that proposals submitted after January 18, 2011, include a data management plan. Clearly, the management and preservation of research data is of growing importance to institutions, and provides a juncture where librarians can work with researchers and other campus professionals to develop research data curation services.
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 | 0.033 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.019 | 0.269 |
| Open science | 0.015 | 0.042 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it