ACRL candidates for 2020: A look at who’s running
Bibliographic record
Abstract
Lynn Silipigni Connaway is the director of library trends and user research at OCLC Research, a position she has held since 2018. Prior to this, Connaway served as senior research scientist and director of user research (2016-18), senior research scientist (2007-16), and consulting research scientist III (2003-07), all at OCLC Research. She was vice-president of research and library systems at NetLibrary (1999-2003), and director and associate clinical professor of the Library and Information Services Department at the University of Denver (1995-99). She served as assistant professor in the School of Library and Informational Science at the University of Missouri (1993-95), and as head of technical services and cataloging at Mesa State College Library (1984-89).Julie Garrison is dean of university libraries at Western Michigan University, a position she has held since 2016. Prior to this, Garrison served as associate dean, research and instructional services at Grand Valley State University Libraries (2009-16); director of off-campus library services at Central Michigan University (2003-07); and as assistant/associate director of public services at Duke University Medical Center Library (2000-02).
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.209 | 0.067 |
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".