Virtual Writing University Archive partnership with the University of Iowa Libraries
Bibliographic record
Abstract
Two sets of panelists, each with a librarian and an LIS faculty member, discussed the process and strategies for collaborating to bridge theory and praxis. The panelists discussed how "researching together" informs their work and will encourage others to engage collaboratively in research and writing efforts. Panelists will discuss their collaborative work and engage directly with attendees to explore the process of collaboration, from conception, through methodology and findings, to communication of results.\nSpeakers: John M. Budd, Professor, School of Information Science and Learning Technologies, University of Missouri; Nicole Saylor, Head of Digital Library Services, University of Iowa Libraries; James Elmborg, Associate Professor, School of Library and Information Science, University of Iowa; Sheril Hook, Collaborative Curriculum Development Librarian, University of Toronto
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".