The Library Sandbox: Testing Innovative Ideas [Invited Speaker]
Bibliographic record
Abstract
Today's presentation examines the Information Commons as an arena for testing innovative ideas in technology, staffing, and scope of service delivery. It also traces the evolution of the Information Commons as it has been driven by changes in user needs, pedagogical theory, technology, and society (from computer lab, to Information Commons, to Learning Commons, to Teaching/Research Commons). David Lewis's 2007 article "A strategy for academic libraries in the first quarter of the 21st Century" and the 2005 report of Symposium 2010 "The academic library in 2010" will be discussed with regard to their vision of the Information Commons as the preferred learning environment for the digital age.In addition, we will look at “lessons learned” from selected academic libraries (showcased in "Transforming Library Service through Information Commons”) as these institutions innovatively steer their respective commons into their second iterations. The presentation will conclude with a power point featuring images of innovative commons at a number of outstanding academic institutions including the University of North Carolina Chapel Hill Health Sciences Library, Temple University's TECH Center, Emory University's Cox Computing Center, North Carolina State University Library’s Learning Commons,University of Massachusetts Amherst Learning Commons, SUNY Binghamton, Guelph University, Indiana University Bloomington, and others.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".