Convergence and Collaboration of Campus Information Services
Bibliographic record
Abstract
Illustrations Preface Chapter 1: Introduction Peter Hernon and Ronald R. Powell Chapter 2: Innovation is an Ongoing Process: Collaboration at the University of California, Irvine Carol Ann Hughes Chapter 3: Sowing an Old Field with a New Crop: Collaborative Services of Libraries and Other Campus Units Richard W. Meyer and Tyler O. Walters Chapter 4: From Isolation to Engagement: Strategy, Structure, and Process Barbara J. Kriigel and Timothy F. Richards Chapter 5: Convergence and Collaboration in Information Services at the University of Calgary Darlene Warren Chapter 6: The Library as Model of Integrated Student-Centered Academic Support Enterprise Jay Schafer and Anne C. Moore Chapter 7: The University of Georgia Student Learning Center Florence E. King, Carla Wilson Buss, Nadine Cohen, Deborah Stanley, and Elizabeth White Chapter 8: From Faction to Fusion: The Columbia University Libraries as Information Services Enterprise James Neal Chapter 9: Libraries and Convergence at Yale Alice Prochaska Chapter 10: The Poetry Center at Suffolk University Fred Marchant and Robert E. Dugan Chapter 11: Collaborative Initiatives to Deliver Agricultural Information Barbara Hutchison, Jeanne Pfander, and George Ruyle Chapter 12: Other Perspectives and Concluding Thoughts Peter Hernon, Ronald R. Powell, and Amy F. Fyn Bibliography Index About the Editors and Contributors
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.030 | 0.025 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".