Great Expectations: Leading Libraries Through the Minefield of Continuous Change
Bibliographic record
Abstract
If there is one thing all library administrators and managers can be sure of, it is that our space, our collections, our systems and our leadership will be impacted by change. Managing that change is critical if managers, directors, deans in our libraries will be able to continue to meet the needs of our communities with different tools and resources. This lively discussion will feature brief presentations about how libraries at Carnegie Mellon University and at Kresge Business Administration Library (University of Michigan) have changed in recent history. The presenters will include what worked well and what worked not as well at the two institutions. They will focus on two areas. First, Denise Novak will explore change through five key aspects: nature, process, role, culture and staff participation of change. Second, Corey Seeman will explore change as defined by six key terms: inevitability, rapidity, flexibility, hospitality, accountability, and empathy. Participants at the meeting will be invited to share how change is managed at their institutions and what issues might be present or on the horizon.
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 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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.050 | 0.031 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".